{
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  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2"
    },
    "colab": {
      "name": "决策树.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "XDsp6A_Boxwd",
        "RnWfkNzXoxxI",
        "PsE3xm5loxxc"
      ],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sunyingjian/-Logging-related-network/blob/master/%E5%BB%B6%E5%AE%89%E6%B2%B9%E7%94%B0%E5%86%B3%E7%AD%96%E6%A0%91.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cqy3vXXOMI06",
        "colab_type": "text"
      },
      "source": [
        "### <font size=5px color=\"red\">✦ *Google Colab 突破90分钟自动断开:</font>\n",
        "<p><font size=3px > 每60分钟自动运行代码以刷新90分钟断开限制. 打开 developer-settings (在你的浏览器) 快速健 Ctrl+Shift+I 然后按console 输入以下代码 Enter. ( mac 按 Option+Command+I)</p><b>复制以下隐藏代码粉贴在浏览器console！！不要关闭浏览器以免失效</b>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Z8cK8h2Avbv",
        "colab_type": "text"
      },
      "source": [
        "<code>function ClickConnect(){\n",
        "console.log(\"Working\"); \n",
        "document.querySelector(\"colab-connect-button\").click() \n",
        "}setInterval(ClickConnect,6000)</code>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtClEMAMLVHw",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f2807e63-ae6a-410b-da3c-2c95eac544c1"
      },
      "source": [
        "#@markdown <h3>← 输入了代码后运行以防止断开</h>\n",
        "\n",
        "\n",
        "import IPython\n",
        "from google.colab import output\n",
        "\n",
        "display(IPython.display.Javascript('''\n",
        " function ClickConnect(){\n",
        "   btn = document.querySelector(\"colab-connect-button\")\n",
        "   if (btn != null){\n",
        "     console.log(\"Click colab-connect-button\"); \n",
        "     btn.click() \n",
        "     }\n",
        "   \n",
        "   btn = document.getElementById('ok')\n",
        "   if (btn != null){\n",
        "     console.log(\"Click reconnect\"); \n",
        "     btn.click() \n",
        "     }\n",
        "  }\n",
        "  \n",
        "setInterval(ClickConnect,60000)\n",
        "'''))\n",
        "\n",
        "print(\"Done.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              " function ClickConnect(){\n",
              "   btn = document.querySelector(\"colab-connect-button\")\n",
              "   if (btn != null){\n",
              "     console.log(\"Click colab-connect-button\"); \n",
              "     btn.click() \n",
              "     }\n",
              "   \n",
              "   btn = document.getElementById('ok')\n",
              "   if (btn != null){\n",
              "     console.log(\"Click reconnect\"); \n",
              "     btn.click() \n",
              "     }\n",
              "  }\n",
              "  \n",
              "setInterval(ClickConnect,60000)\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bb3q51GToxvK",
        "colab_type": "text"
      },
      "source": [
        "# Facies classification using Machine Learning\n",
        "\n",
        "#### Brendon Hall, [Enthought](https://www.enthought.com/)\n",
        "\n",
        "This notebook demonstrates how to train a machine learning algorithm to predict facies from well log data.  The dataset we will use comes from a class excercise from The University of Kansas on [Neural Networks and Fuzzy Systems](http://www.people.ku.edu/~gbohling/EECS833/).  This exercise is based on a consortium project to use machine learning techniques to create a reservoir model of the largest gas fields in North America, the Hugoton and Panoma Fields. For more info on the origin of the data, see [Bohling and Dubois (2003)](http://www.kgs.ku.edu/PRS/publication/2003/ofr2003-50.pdf) and [Dubois et al. (2007)](http://dx.doi.org/10.1016/j.cageo.2006.08.011). \n",
        "\n",
        "The dataset we will use is log data from nine wells that have been labeled with a facies type based on oberservation of core.  We will use this log data to train a support vector machine to classify facies types.  Support vector machines (or SVMs) are a type of supervised learning model that can be trained on data to perform classification and regression tasks.  The SVM algorithm uses the training data to fit an optimal hyperplane between the different classes (or facies, in our case).  We will use the SVM implementation in [scikit-learn](http://scikit-learn.org/stable/modules/svm.html).\n",
        "\n",
        "First we will [explore the dataset](#Exploring-the-dataset).  We will load the training data from 9 wells, and take a look at what we have to work with.  We will plot the data from a couple wells, and create cross plots to look at the variation within the data.  \n",
        "\n",
        "Next we will [condition the data set](#Conditioning-the-data-set).  We will remove the entries that have incomplete data.  The data will be scaled to have zero mean and unit variance.  We will also split the data into training and test sets.\n",
        "\n",
        "We will then be ready to [build the SVM classifier](#Building-the-SVM-classifier).  We will demonstrate how to use the cross validation set to do [model parameter selection](#Model-parameter-selection).\n",
        "\n",
        "Finally, once we have a built and tuned the classifier, we can [apply the trained model](#Applying-the-classification-model-to-new-data) to classify facies in wells which do not already have labels.  We will apply the classifier to two wells, but in principle you could apply the classifier to any number of wells that had the same log data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9jd9-aSoxvN",
        "colab_type": "text"
      },
      "source": [
        "## Exploring the dataset\n",
        "\n",
        "First, we will examine the data set we will use to train the classifier.  The training data is contained in the file `facies_vectors.csv`.  The dataset consists of 5 wireline log measurements, two indicator variables and a facies label at half foot intervals.  In machine learning terminology, each log measurement is a feature vector that maps a set of 'features' (the log measurements) to a class (the facies type).  We will use the pandas library to load the data into a dataframe, which provides a convenient data structure to work with well log data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFEXKoC6o2wB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "f12d0dd5-a2b7-4fa5-bc8b-195909c83639"
      },
      "source": [
        "!git clone https://github.com/seg/tutorials-2016.git\n",
        "! git clone https://github.com/sunyingjian/Machine-Learning-From-Scratch.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'tutorials-2016'...\n",
            "remote: Enumerating objects: 161, done.\u001b[K\n",
            "remote: Total 161 (delta 0), reused 0 (delta 0), pack-reused 161\u001b[K\n",
            "Receiving objects: 100% (161/161), 16.86 MiB | 25.47 MiB/s, done.\n",
            "Resolving deltas: 100% (64/64), done.\n",
            "Cloning into 'Machine-Learning-From-Scratch'...\n",
            "remote: Enumerating objects: 287, done.\u001b[K\n",
            "remote: Total 287 (delta 0), reused 0 (delta 0), pack-reused 287\u001b[K\n",
            "Receiving objects: 100% (287/287), 91.24 KiB | 3.65 MiB/s, done.\n",
            "Resolving deltas: 100% (133/133), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rJBO73lpHA7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "7792fd5c-c29d-4288-92fc-24e19561b948"
      },
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "path = \"/content/drive/Facies_classification\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5phM8LfoxvP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "outputId": "2c3aeece-7234-4839-ef9f-70294038110b"
      },
      "source": [
        "%matplotlib inline\n",
        "#%matplotlib inline 可以在Ipython编译器里直接使用，功能是可以内嵌绘图，并且可以省略掉plt.show()这一步。\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from pandas import set_option\n",
        "set_option(\"display.max_rows\", 10)#设置要显示的默认行数，显示的最大行数是10\n",
        "pd.options.mode.chained_assignment = None #为了在增加列表行数的时候防止出现setting with copy warning\n",
        "filename = 'facies_vectors.csv'\n",
        "training_data = pd.read_csv('/content/tutorials-2016/1610_Facies_classification/training_data.csv')\n",
        "training_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Facies</th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.0</td>\n",
              "      <td>77.450</td>\n",
              "      <td>0.664</td>\n",
              "      <td>9.900</td>\n",
              "      <td>11.915</td>\n",
              "      <td>4.600</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.5</td>\n",
              "      <td>78.260</td>\n",
              "      <td>0.661</td>\n",
              "      <td>14.200</td>\n",
              "      <td>12.565</td>\n",
              "      <td>4.100</td>\n",
              "      <td>1</td>\n",
              "      <td>0.979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.0</td>\n",
              "      <td>79.050</td>\n",
              "      <td>0.658</td>\n",
              "      <td>14.800</td>\n",
              "      <td>13.050</td>\n",
              "      <td>3.600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.5</td>\n",
              "      <td>86.100</td>\n",
              "      <td>0.655</td>\n",
              "      <td>13.900</td>\n",
              "      <td>13.115</td>\n",
              "      <td>3.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2795.0</td>\n",
              "      <td>74.580</td>\n",
              "      <td>0.647</td>\n",
              "      <td>13.500</td>\n",
              "      <td>13.300</td>\n",
              "      <td>3.400</td>\n",
              "      <td>1</td>\n",
              "      <td>0.915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3227</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3120.5</td>\n",
              "      <td>46.719</td>\n",
              "      <td>0.947</td>\n",
              "      <td>1.828</td>\n",
              "      <td>7.254</td>\n",
              "      <td>3.617</td>\n",
              "      <td>2</td>\n",
              "      <td>0.685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3228</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3121.0</td>\n",
              "      <td>44.563</td>\n",
              "      <td>0.953</td>\n",
              "      <td>2.241</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.344</td>\n",
              "      <td>2</td>\n",
              "      <td>0.677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3229</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3121.5</td>\n",
              "      <td>49.719</td>\n",
              "      <td>0.964</td>\n",
              "      <td>2.925</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.190</td>\n",
              "      <td>2</td>\n",
              "      <td>0.669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3230</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3122.0</td>\n",
              "      <td>51.469</td>\n",
              "      <td>0.965</td>\n",
              "      <td>3.083</td>\n",
              "      <td>7.708</td>\n",
              "      <td>3.152</td>\n",
              "      <td>2</td>\n",
              "      <td>0.661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3231</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3122.5</td>\n",
              "      <td>50.031</td>\n",
              "      <td>0.970</td>\n",
              "      <td>2.609</td>\n",
              "      <td>6.668</td>\n",
              "      <td>3.295</td>\n",
              "      <td>2</td>\n",
              "      <td>0.653</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3232 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Facies Formation        Well Name   Depth  ...   PHIND     PE  NM_M  RELPOS\n",
              "0          3     A1 SH        SHRIMPLIN  2793.0  ...  11.915  4.600     1   1.000\n",
              "1          3     A1 SH        SHRIMPLIN  2793.5  ...  12.565  4.100     1   0.979\n",
              "2          3     A1 SH        SHRIMPLIN  2794.0  ...  13.050  3.600     1   0.957\n",
              "3          3     A1 SH        SHRIMPLIN  2794.5  ...  13.115  3.500     1   0.936\n",
              "4          3     A1 SH        SHRIMPLIN  2795.0  ...  13.300  3.400     1   0.915\n",
              "...      ...       ...              ...     ...  ...     ...    ...   ...     ...\n",
              "3227       5      C LM  CHURCHMAN BIBLE  3120.5  ...   7.254  3.617     2   0.685\n",
              "3228       5      C LM  CHURCHMAN BIBLE  3121.0  ...   8.013  3.344     2   0.677\n",
              "3229       5      C LM  CHURCHMAN BIBLE  3121.5  ...   8.013  3.190     2   0.669\n",
              "3230       5      C LM  CHURCHMAN BIBLE  3122.0  ...   7.708  3.152     2   0.661\n",
              "3231       5      C LM  CHURCHMAN BIBLE  3122.5  ...   6.668  3.295     2   0.653\n",
              "\n",
              "[3232 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FyP3GWPwoxvV",
        "colab_type": "text"
      },
      "source": [
        "Remove a single well to use as a blind test later."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUAnZ1GCoxvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "blind = training_data[training_data['Well Name'] == 'SHANKLE']\n",
        "#抽出一部分作为验证集\n",
        "training_data = training_data[training_data['Well Name'] != 'SHANKLE']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVs7iID7oxvZ",
        "colab_type": "text"
      },
      "source": [
        "This data is from the Council Grove gas reservoir in Southwest Kansas.  The Panoma Council Grove Field is predominantly a carbonate gas reservoir encompassing 2700 square miles in Southwestern Kansas.  This dataset is from nine wells (with 4149 examples), consisting of a set of seven predictor variables and a rock facies (class) for each example vector and validation (test) data (830 examples from two wells) having the same seven predictor variables in the feature vector.  Facies are based on examination of cores from nine wells taken vertically at half-foot intervals. Predictor variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge. These are essentially continuous variables sampled at a half-foot sample rate. \n",
        "\n",
        "The seven predictor variables are:\n",
        "* Five wire line log curves include [gamma ray](http://petrowiki.org/Gamma_ray_logs) (GR), [resistivity logging](http://petrowiki.org/Resistivity_and_spontaneous_%28SP%29_logging) (ILD_log10),\n",
        "[photoelectric effect](http://www.glossary.oilfield.slb.com/en/Terms/p/photoelectric_effect.aspx) (PE), [neutron-density porosity difference and average neutron-density porosity](http://petrowiki.org/Neutron_porosity_logs) (DeltaPHI and PHIND). Note, some wells do not have PE.\n",
        "* Two geologic constraining variables: nonmarine-marine indicator (NM_M) and relative position (RELPOS)\n",
        "\n",
        "The nine discrete facies (classes of rocks) are: \n",
        "1. Nonmarine sandstone\n",
        "2. Nonmarine coarse siltstone \n",
        "3. Nonmarine fine siltstone \n",
        "4. Marine siltstone and shale \n",
        "5. Mudstone (limestone)\n",
        "6. Wackestone (limestone)\n",
        "7. Dolomite\n",
        "8. Packstone-grainstone (limestone)\n",
        "9. Phylloid-algal bafflestone (limestone)\n",
        "\n",
        "These facies aren't discrete, and gradually blend into one another. Some have neighboring facies that are rather close.  Mislabeling within these neighboring facies can be expected to occur.  The following table lists the facies, their abbreviated labels and their approximate neighbors.\n",
        "\n",
        "这里说的是相邻相的问题\n",
        "\n",
        "Facies |Label| Adjacent Facies\n",
        ":---: | :---: |:--:\n",
        "1 |SS| 2\n",
        "2 |CSiS| 1,3\n",
        "3 |FSiS| 2\n",
        "4 |SiSh| 5\n",
        "5 |MS| 4,6\n",
        "6 |WS| 5,7\n",
        "7 |D| 6,8\n",
        "8 |PS| 6,7,9\n",
        "9 |BS| 7,8\n",
        "\n",
        "Let's clean up this dataset.  The 'Well Name' and 'Formation' columns can be turned into a categorical data type.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RruElreioxva",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "3f79a10b-6904-4f70-bee1-2a7b364dd1e0"
      },
      "source": [
        "training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
        "training_data['Formation'] = training_data['Formation'].astype('category')\n",
        "training_data['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SHRIMPLIN, LUKE G U, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]\n",
              "Categories (7, object): [SHRIMPLIN, LUKE G U, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY,\n",
              "                         CHURCHMAN BIBLE]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCHjec61oxvd",
        "colab_type": "text"
      },
      "source": [
        "These are the names of the 10 training wells in the Council Grove reservoir.  Data has been recruited into pseudo-well 'Recruit F9' to better represent facies 9, the Phylloid-algal bafflestone. \n",
        "\n",
        "Before we plot the well data, let's define a color map so the facies are represented by consistent color in all the plots in this tutorial.  We also create the abbreviated facies labels, and add those to the `facies_vectors` dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAVy0Tvfoxvh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "5beeca3c-b1e6-4621-84f5-68472df9068d"
      },
      "source": [
        "# 1=sandstone  2=c_siltstone   3=f_siltstone \n",
        "# 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite\n",
        "# 8=packstone 9=bafflestone\n",
        "facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
        "       '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
        "\n",
        "facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
        "                 'WS', 'D','PS', 'BS']\n",
        "#facies_color_map is a dictionary that maps facies labels\n",
        "#to their respective colors\n",
        "facies_color_map = {}\n",
        "for ind, label in enumerate(facies_labels):\n",
        "    facies_color_map[label] = facies_colors[ind]\n",
        "\n",
        "def label_facies(row, labels):\n",
        "    return labels[ row['Facies'] -1]\n",
        "    \n",
        "training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1) #建立标签\n",
        "training_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Facies</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.558390</td>\n",
              "      <td>2873.456522</td>\n",
              "      <td>66.249445</td>\n",
              "      <td>0.644637</td>\n",
              "      <td>3.754963</td>\n",
              "      <td>13.118929</td>\n",
              "      <td>3.805693</td>\n",
              "      <td>1.523895</td>\n",
              "      <td>0.523057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.515249</td>\n",
              "      <td>138.349383</td>\n",
              "      <td>31.610849</td>\n",
              "      <td>0.241922</td>\n",
              "      <td>5.045916</td>\n",
              "      <td>7.389665</td>\n",
              "      <td>0.894118</td>\n",
              "      <td>0.499518</td>\n",
              "      <td>0.287499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2573.500000</td>\n",
              "      <td>13.250000</td>\n",
              "      <td>-0.025949</td>\n",
              "      <td>-21.832000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2767.500000</td>\n",
              "      <td>46.081500</td>\n",
              "      <td>0.497000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>8.165000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.276500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>2895.000000</td>\n",
              "      <td>65.541000</td>\n",
              "      <td>0.627000</td>\n",
              "      <td>3.581000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.529000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>2987.000000</td>\n",
              "      <td>80.714000</td>\n",
              "      <td>0.812323</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>16.144000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.771500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>3122.500000</td>\n",
              "      <td>361.150000</td>\n",
              "      <td>1.480000</td>\n",
              "      <td>18.500000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>8.094000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Facies        Depth  ...         NM_M       RELPOS\n",
              "count  2783.000000  2783.000000  ...  2783.000000  2783.000000\n",
              "mean      4.558390  2873.456522  ...     1.523895     0.523057\n",
              "std       2.515249   138.349383  ...     0.499518     0.287499\n",
              "min       1.000000  2573.500000  ...     1.000000     0.010000\n",
              "25%       2.000000  2767.500000  ...     1.000000     0.276500\n",
              "50%       4.000000  2895.000000  ...     2.000000     0.529000\n",
              "75%       7.000000  2987.000000  ...     2.000000     0.771500\n",
              "max       9.000000  3122.500000  ...     2.000000     1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMJylw1foxvk",
        "colab_type": "text"
      },
      "source": [
        "This is a quick view of the statistical distribution of the input variables.  Looking at the `count` values, most values have 4149 valid values except for `PE`, which has 3232.  In this tutorial we will drop the feature vectors that don't have a valid `PE` entry."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-Scfnbsoxvl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PE_mask = training_data['PE'].notnull().values\n",
        "training_data = training_data[PE_mask]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24iOGnKzoxvo",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at the data from individual wells in a more familiar log plot form.  We will create plots for the five well log variables, as well as a log for facies labels.  The plots are based on the those described in Alessandro Amato del Monte's [excellent tutorial](https://github.com/seg/tutorials/tree/master/1504_Seismic_petrophysics_1)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3l3m-Xj7oxvp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_facies_log_plot(logs, facies_colors):\n",
        "    #确认测井数据是以深度排序的\n",
        "    logs = logs.sort_values(by='Depth')\n",
        "    cmap_facies = colors.ListedColormap(\n",
        "            facies_colors[0:len(facies_colors)], 'indexed')\n",
        "    \n",
        "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
        "    \n",
        "    cluster=np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
        "    \n",
        "    f, ax = plt.subplots(nrows=1, ncols=6, figsize=(8, 12))\n",
        "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
        "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
        "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
        "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
        "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
        "    im=ax[5].imshow(cluster, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    \n",
        "    divider = make_axes_locatable(ax[5])\n",
        "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
        "    cbar=plt.colorbar(im, cax=cax)\n",
        "    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
        "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
        "                                ' PS ', ' BS ']))\n",
        "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
        "    \n",
        "    for i in range(len(ax)-1):\n",
        "        ax[i].set_ylim(ztop,zbot)\n",
        "        ax[i].invert_yaxis()\n",
        "        ax[i].grid()\n",
        "        ax[i].locator_params(axis='x', nbins=3)\n",
        "    \n",
        "    ax[0].set_xlabel(\"GR\")\n",
        "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
        "    ax[1].set_xlabel(\"ILD_log10\")\n",
        "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
        "    ax[2].set_xlabel(\"DeltaPHI\")\n",
        "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
        "    ax[3].set_xlabel(\"PHIND\")\n",
        "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
        "    ax[4].set_xlabel(\"PE\")\n",
        "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
        "    ax[5].set_xlabel('Facies')\n",
        "    \n",
        "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
        "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
        "    ax[5].set_xticklabels([])\n",
        "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08tHuzWQoxvu",
        "colab_type": "text"
      },
      "source": [
        "Placing the log plotting code in a function will make it easy to plot the logs from multiples wells, and can be reused later to view the results when we apply the facies classification model to other wells.  The function was written to take a list of colors and facies labels as parameters.  \n",
        "\n",
        "We then show log plots for wells `SHRIMPLIN`.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvcHuB6koxvu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "outputId": "11308d8b-dc10-4f23-e233-bf026af63e9b"
      },
      "source": [
        "make_facies_log_plot(\n",
        "    training_data[training_data['Well Name'] == 'SHRIMPLIN'],\n",
        "    facies_colors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x864 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZAztUeEoxvx",
        "colab_type": "text"
      },
      "source": [
        "In addition to individual wells, we can look at how the various facies are represented by the entire training set.  Let's plot a histgram of the number of training examples for each facies class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a20k_2rQoxvy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "4ae7e4a2-bae8-42ea-85ca-b6218434d70d"
      },
      "source": [
        "#count the number of unique entries for each facies, sort them by facies number (instead of by number of entries)\n",
        "#计算每个相的唯一条目数，然后按相数（而不是条目数）对它们进行排序\n",
        "facies_counts = training_data['Facies'].value_counts().sort_index()\n",
        "#use facies labels to index each count\n",
        "#使用相标签索引每个计数\n",
        "facies_counts.index = facies_labels\n",
        "\n",
        "facies_counts.plot(kind='bar',color=facies_colors, \n",
        "                   title='Distribution of Training Data by Facies')\n",
        "facies_counts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SS      170\n",
              "CSiS    649\n",
              "FSiS    498\n",
              "SiSh    177\n",
              "MS      198\n",
              "WS      391\n",
              "D        81\n",
              "PS      458\n",
              "BS      161\n",
              "Name: Facies, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQMPXVknoxv0",
        "colab_type": "text"
      },
      "source": [
        "This shows the distribution of examples by facies for the 3232 training examples in the training set.  Dolomite (facies 7) has the fewest with 141 examples.  There are also only 185 bafflestone examples.  Depending on the performance of the classifier we are going to train, we may consider getting more examples of these facies.\n",
        "\n",
        "这显示了训练集中3232个训练实例的相分布。白云岩(相7)最少，有141个实例。也只有185个莫名其妙的例子。根据我们要训练的分类器的性能，我们可以考虑获得更多的这些相的例子。\n",
        "\n",
        "\n",
        "\n",
        "Crossplots are a familiar tool in the geosciences to visualize how two properties vary with rock type.  This dataset contains 5 log variables, and scatter matrix can help to quickly visualize the variation between the all the variables in the dataset.  We can employ the very useful [Seaborn library](https://stanford.edu/~mwaskom/software/seaborn/) to quickly create a nice looking scatter matrix. Each pane in the plot shows the relationship between two of the variables on the x and y axis, with each point is colored according to its facies.  The same colormap is used to represent the 9 facies.  \n",
        "\n",
        "交会图是地球科学中常见的一种工具，它可以直观地描述两种属性随岩石类型的变化。该数据集包含5个日志变量，散点矩阵可以帮助快速可视化数据集中所有变量之间的变化。我们可以使用非常有用的Seaborn库来快速创建一个漂亮的分散矩阵。图中的每个窗格显示x轴和y轴上的两个变量之间的关系，每个点根据其相进行着色。相同的颜色图用于表示9相。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "ih2MQZ0uoxv0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "564588cb-07b4-4c97-e871-7aae387435c0"
      },
      "source": [
        "#save plot display settings to change back to when done plotting with seaborn\n",
        "inline_rc = dict(mpl.rcParams)\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "sns.pairplot(training_data.drop(['Well Name','Facies','Formation','Depth','NM_M','RELPOS'],axis=1),\n",
        "             hue='FaciesLabels', palette=facies_color_map,\n",
        "             hue_order=list(reversed(facies_labels)))\n",
        "\n",
        "#switch back to default matplotlib plot style\n",
        "mpl.rcParams.update(inline_rc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n",
            "/usr/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
            "The datapath rcparam was deprecated in Matplotlib 3.2.1 and will be removed two minor releases later.\n",
            "  self[key] = other[key]\n",
            "/usr/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
            "The savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "  self[key] = other[key]\n",
            "/usr/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
            "The text.latex.unicode rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\n",
            "  self[key] = other[key]\n",
            "/usr/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
            "The verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "  self[key] = other[key]\n",
            "/usr/lib/python3.6/_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
            "The verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
            "  self[key] = other[key]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xW0y58iiMS+/Ot7Lqka/f/hhv/soh2WPzqQWzURVKphgIUUDpsvmjC48m3von7l906oh1o1I6XS19PL/utcx1hpRTUSpyK6jIhjbIhw5Z97QAaEE06SEXk1iiL8UL/Y0OgGhHguf/+VUijkfQcVCWnb8TZrI7cxEN0NGynQ2LzoJkd34+X4gxchwXZdnDYt3wtKxy8aGPhzKNcZDYLaZheRTvGlaPPHjt33Ho39QQ7UjwwvptGJ6MXhCi0BzHRY91cP+iM/daNxqelmmMA1JORUlJg7zI0nPIB4asWwNPeikgAJoJbqI0CRSiDLi2mzlJpkU7ElCAm9aOk8qctNM6WrbjDi6bQpQDl6xyEQgjsVsmRqpHAmH/caHqLyHEcKM6rw+pT0HKqSgdaZAXkeM6uJ4/7DDdQ57KMYfckx5yMcnphk5FfSjrbxX1oYLUWEqZ1DfPyPpbffMMdBXI/8GE+CB0ssqFFUdit0yMVI9Y/afyQtVfQojhRnVeH1KfgpRTUToSdkWUXsAtu4d86Crr/Yu6yRxyMYmFKk0+tWB25mSZnttlawWYFxusYf7azZmTd3quGcGa/B9LiA/A1ryscvGX/04w/06J3bKQox75+u2P8affdhe2/hJCDDeK87qteZz87aOKc50hxD7Iom5FlG58K22ghzxryDr+kHVPAzwZsi4mL03X0CoUn1l8jD98TPdPnoVYaMVxPCKNB7Nw01Zcx/LvoAdrZFEsUXYcx0UNKRee8iR2y8CwesQwCQRrabiYgtZfQojhRnNedxyXKc2VRbnOEGJfpEFeRJkGuT6w7ZmdNWQ95feQo6RBLiY9x3EHtg0t8PnRcTwwqsHo36pUGjSiTA0rFy4Su2ViaD3i2P05Jdf3QhTdaM7rmq6RdKWcitKTIetFlO4NN5SB0v2fPpU1ZN3uX2XdRPOSaFI7CCGEEEIIIcSEVZIe8s7OTpYsWcJ7772HaZrMmDGDW265hfr6eg477DAOPfRQ9P4G66pVqzjssMMAePbZZ1m1ahWO43DkkUdy2223EQ6HS/EV9kt6z3Gl6yjd/+mtoT3kKDytf5EJLwFapMipFGJ8Ukonpet536dciFKT2C5fkjdCFJ+UOzHRlKRBrmkal1xyCccffzwAK1euZPXq1Xzve98D4OGHH6aioiLrPdFolBtvvJEf//jHzJw5k+uvv54NGzZwxRVXFD39+8vOzCFXmR7yrAY56SHrQf+fXlwa5EKMglI6u1Muix/fRkt3kuaaIGvOOYJGU5eTtBjXJLbLl+SNEMUn5U5MRCUZsl5bW5tpjAMcc8wxtLS07PU9zz//PLNnz2bmzJkAnH/++fzyl78sZDLzLrPKutIHzSHvX9TN8/xtz/C3PQPQZKV1IUYlpessfvwNWrqTALR0J1n8+Buk9NFVcUppKLsHkm0ouweltEImV4h9SsekE9tDd9suGiL+dj1jjW1ROLnqnfUvvIuX6pa6RIgCcZVOd9su7vhMLf/8xWk0RAJSJ4pxr+SLurmuy0MPPcQpp5yS+dvXvvY1HMfh5JNPZuHChZimSWtrK83NzZnXNDc309raWook77f0HHJdU+hafw+5m+4ht9HwBrY9AzQvVopkCjHu2K6XuShOa+lOYrseah/vVUojtvttNiw6i46W7ZntUSKNB8tq1aIkcsXktbc/yu1U8lpr36hjWxTW0HrnqKZK5s9K8s8XnSB1iRAFoJRGtPVPPP2PZ2bXjf8TkDpRjGslb5AvX76cSCTCV7/6VQB++9vf0tTURF9fH1dffTX33HMPixcvzsuxGhoq8/I5I5k6tWqvz1f0mQBUVYSpq63A0A10Q2Pq1Co8J0asFYKhELpZTaoXaqo8VNXeP3Msxy+0yXz8Qhy70PE6VKnzb6ixpKetL0VzTTDr4ri5JkjINJhSufdpHz1tOzMNH4COlu1sWHQWi3/8X9RPnbZf6SmGcktPvuO13L5fIYz0HXPF5GPXfoUFq/6dK1r7Rh3b5aIc83Ks8ZrrOwytdxYcW8ljS/52n3VJPpXjb5smacufscRruX63fKSrp20n9y86c1jduPCfntnvOrFcfy8o77SJ/Cppg3zlypVs376ddevWZRZxa2pqAqCyspJzzz2XjRs3Zv7+0ksvZd7b0tKSee1otbf34bqFuUs9dWoVe/b07vU1e9p7AEgmbbq6Yxi6TjQW99/ndBMBEkkXx/Jnkfd0tWMn9v6ZYzl+IU3m4w89dr4q0ELG61Clzr+hxpoepXTWnHNEZvhoek6ZnrLYsye59zcn45mTe1pHy3ZSiUQmDeP999nXZ+VDPuO13H7vQtjrdxwhJutMb2yxXQbKMVZhbPE60ncYWu9MCbHPuiSfyrmcSNpKE6/l+rvnLV0j1I0za4z9qhPL9feC4qdNGv+lVbIG+fe//322bdvGD3/4Q0zT7znu7u4mGAwSCoWwbZstW7Zw+OGHA3DSSSexfPly3n33XWbOnMnDDz/MF77whVIlf79k5pD3zx9Xusr8TSPV/yoZsi7EWDmOS6Ops3Hu0WNedVUpk/rmGVkn+frmGegqMLDfsxBFNFJMTqurYuPcD8uKwmViaL0TTnVJXSJEAY1UNwYCptSJYlwryQoIb731FuvXr2f37t2cf/75nHnmmVx++eW88847nHvuuZxxxhmcccYZGIbBokWLAL/H/JZbbmHBggV87nOfo7e3l3nz5pUi+fstPYdcaQMNctvtP017/Yu7aYO3PYsWO4lClJwZUES7E7gBg5Rh4JoGSu27qnIcF2XZBB0HZdmjPzkHa5i/djP1zTMAMvM+CdZ8kK8hRE5K6TgBg5buBE5ghNjOGZNPEA7VUWF5mOiYAZktWW68cB3z1z4xLN+C4XpCKMLKkHwT4oMY6XxtVud8ebq+TSqFaxpEzICURVGWStJDfsghh/Dmm2/mfO7JJ58c8X2nnnoqp556aqGSVXADq6z7P7vfQ55eZd3vIfcIgOaPGNDcgR7y5H8+iGo6AmPmnCKmWIjiMgMKN+7yTiLFkl++WZQtTRzHI9J4MAs3bcV1LHQVgGCNLMIk8m602/UMjUllmGheFc+s/gPRjgQV9SE+9a3ZmNUGKUv6XkslV37ee+6RLHzgRVw7JfkmRJ6N5Xydq3yu+sJh7HjkbZI9KT71rdm4ldKrLsqD7BFQROkV1VX/kHVDV5m9ybVBPeRoBh4G9A9Zt1veILl1I7GfLyt+ooUoIuVqtHUlM41xKM42T47j4RjVeMEGHKNaGuOiIMayPd/gmAyYdTx3z2tEOxIARDsSvPDDbShXttQqpVz5edkjr5MK1Um+CVEgoz1f5yqfS375JjO/MCNTFuM9qZzvFaLYSr7K+mQyMIfcv/gaPIc83UOO1p8lWjDTQ+5s/3/+n4qXVCFKwnM8CKr93sJsrJTSINmN46RQypSecVFQ+7s9n+d4mUZdWrQj4ZeXUZJYz7995efgfKufWcmhf1NDIAxWshMVlBt/QhTS4PJ5VFMlC46tpM70qK9KUj+zko53+3ClDIoyIQ3yIsrMIdf9n93Q1aB9yP3nvP4s8bQguHH/cazT/38qhhfvQgvXFjHVQhSPpjRIOjm3MDN0jXyujCT7j4tiM3Rtv2JbUxoV9aGsRnlFfcgvL6MoExLrhbGv/EznW7Da4NDP6vzous/L7y9EkaTLZ0MkwLUfczNbEtY3z+Br33uUv/y+AX2UdagQhSZD1ovIzrHKuu2kF3UbtMo6+D3k/UPW3VhX5jO87taipFWIUnB0jym1/jyv5hp/t4H0PFvTzfNcr2R3zv3HSXbn9zhC9DNdlzXnHDHm2HZ0j099azYV9f6Cn+m5yI4+ysacxHpB7Cs/0/n20c/V8aPrviK/vxBFlC6fC+dU89i12eXvR9d9haO+PIVwtVniVArhkx7yIho6ZN0YvO1ZZg55oP//QfDSPeRdEAiBlcDt2Y0+7fDiJlyIIklZDmZY8ZFIiE1zj8b2wNAh4OTe5kkpjTAxdNfC1QPEieyzxykYUhBQpNqtnPuZuo4lNeMksz9xtD8Gb5PlaRqa541qC7OU5RBpMDn1huPw0NDwwLGJxaxRHjclsV4Ag/PTAQylEQnoeHgYlkMy4WBWG1TpgXHz+xerLIiJo5Axkz5f+5M2PegvV6ORLp8NtUbu8oeFbki/pCgPZXYqmNjSjW/VP09c13Xs9JD1dIM8PWjBTqFZneC5eLFOjKmzsFtex+vdVeRUC1FcKcuhZmqE2J5e0veuc51+ldIIx3ew+0dXYXe2YtQ10fi11cTD00e8GAiGFJYyePn9JIcFAxz1mTM57syLiNTUE+vu4HdPPCB7Bk8y+xNHH4TjuCjHZerUKvbs6d1rrKXnfbuuRdIL8maiip1Rl0hAY05zkGBIjeridKS9eyXWPzjH8fPDUgZ/akvRpHUT8FKEQyFCkRoScQelAuPi9y92WRDjXyFjZvD5OmZ5g+o9SCacUa2L4TjuyOVPD3yg9AmRT3JrqIgyc8jV8FXWs7Y9A/S+FjS7Gz32Ll6sE722CVQAt2d38RMuRBkKE8tcBADYna3s/tFVhImN/KaA4uUW/+TeRg2f//aN/GzVYn6+5hoMM8gZ/7gKTdP8E72YFPYrjgpMKQ3l9OD0trLz7f/hwSXnc/fXPsm0vrepDWrELI+XW5L9PUejMNLevcGaAn6LSSSg+FNbiml9b/PMHZfT/tYrdO14h2RnK2ZAHze/fzmWBVHeChozg87XADHL409tKZx4N1qyHTe6i8dvu5xbv/AR7v7GicR2v5373D1Oyp+Y3KSHvIjSPeS6puF4nj+H3PXvj2sM2vbMSYILKFC9/4sX70ELVaKFa3B7pUEuBIDuWpmLgDS7sxXds0d4B4CWOblPoZv7F/8dVVOmcfrCFTy0bL4suDQJ7V8cFU6uBdguuHkDT919PQ8uPouL7tvKH5JV/XE8uhtHY9m7V+wPjSatm2fW3cTJcxdm1yV3/ozIAYeMi9+/3MqCKH+FjZmB8zVAbVBjWt/b3PXN7Lqxp30n2199iQ2LzmLhpq1gVGd9itR/YjyQHvIislybgDLQNP8iysi17RkBNKsXHA100GLvAh6aEUQLVfnzyYUQuHoAo64p629GXROutrf7jP6wNwDD9efVnjrvmswFNMiCS5PN/sVRAeVYgO2hZfM5dd41dLRsx3D9c4Ufx6O/oBzt3r1if3gEvBTHnXnR8LrkyrP7h9WW/+9fdmVBlL3CxszA+RrgoEAPDy7OXTem/+06udfVGA/lT0xu0iAvIsvxG+Refz3g95APGbKu6Wh21O8hB/TEjv4Xm2jBStyYNBKEAIgTofFrqzMXA5m5a0RGfpPlMKc5SCSgYev+vNpITf3ICy6JCW+/4qiARlqALVJTT33zDGzdzMylxCqnGciTmOUQDoWorG8c13VJuZUFUf4KGjODztcAAW/kuhEG1mUQYjyS255FlLItDN0g3auhhqyy7qEBCuxoZhUrze4fom6YEKzA63iv+AkXogw5jkeq8kCmfWsDeDaabuDqigqrG1flXuk1mXAIhuCTB5poTGH+XU/Qs3vHuFhwSRSG43jEw9Np/NYGdM/G1YyCrCw9eCViTwVI9AUIokAHW/MyK62PtABbKh5l/l1PUDN1Cp8EsOxRrzYsCiuZcAhFaqieMi1n3ill+jfgB+XzWOVayTrfilUWxMQxUswAVKp4Jl49t2LMn50+X59wYBANHasnmrN8xbo7sueFS7yKcUga5EVkO5bfQ97/7+xF3Sx/yzNNQ7MGesg1p3+IugqgBSvxEr34T8rgBjG5KaVj9zg8vf5tQtUGJ5xTScej1+xzpddkwoH+hkxk6iyq6qYx/86fseHKs7PmkMuJffJwHI8+woP+UoDG+JCViOu/spL/fLyPRI/NpxbMRlUov7HWvwDR4Dnk8+/8GVUNzThGBYne1L4PKIouEXcwq4bXJV+//TFe+tH7JHvezc7nMRhpJWvPPSTv36PQZUFMPENjRimGxeu0i/4JFWwe880d2/LwuhK8sH4bwWqDr9/+GA9e+3dZdWNFXaM/d1zmhYtxTBrkRWS5NoYyMue3wYu6QQoIgOdlDVlHS6Ip0Pob5HguXiKKFqoqwTcQolrQp/UAACAASURBVHwYnsZv1m8j2pHg+HNn0vHoomErvTZ+a8OQi8sBSukEwiZoIWqr6lj4wIu4dkoWfBF5l2sl4o5Hr+FjX1jLs+vf5YX12/jM4mNwyL0AkQrXogdNdE0j4HlY8dR+97SK/BqoRzTwPKqaDs3kXd8ei//9VScd7/YBZOXzWIy0kvX0yzdBZnNIIcpDrnjd+cA/7vV8PJL0eT5UbXLkWbOI1JpcvmErmmah6f652k6fq+WcLcaxkjTIOzs7WbJkCe+99x6maTJjxgxuueUW6uvreeWVV1i6dCnJZJLp06dzxx130NDQALDX58YDy7ExVYB0i9wYMmQdzZ/7otkx8AJACnRQQR3N8OeQAxDvAmmQi8nOhWhHAoBQhUZ0DCu9KqWjhYK8uCOe2d/0uOZG9ETSb+jIiV3k0UgrEddU+HMjox2JgZuw+I1yjGr/DK10XDPIf2XFahiVjlVRMrnrkTC4tZiOxov/8l9Zrx+az6M1Uvx4dgppkItyk9eV110IVZscO+9I/tBlE2t3iASqOb45jCt1oJhASjLuWdM0LrnkErZs2cKTTz7Jhz70IVavXo3rulx99dUsXbqULVu2MGfOHFavXg2w1+fGC8uxMZTKDABTuo6T7iH3Uv0Ncs+fQ671zw9TfoPcUyZa0J+D48VlpXUh0KGiPgRAIuqNaaXXQNjkdy3xrP1Nf9cS93u6hMizkVYiTkT9+KuoD414NpZYLV97zZtB9VPa3vJ5b0aKH82QGBDlJ68rr+tw1FcO8Rvjg8rZS1IHigkmrw1yyxrdSqK1tbUcf/zxmX8fc8wxtLS0sG3bNoLBIHPmzAHg/PPP5+mnnwbY63PjheVYBHSD9DLrSjcGbXuW3UPuGRV4GKB7fg+5Gughd6OdJUm/EOXE1jw+tWA2FfUh/mdLG/VfWTniSq9KaSi7B5JtKLsHTSNrf1Po/7c2un2dhRiLXCsR139lJf+zpY2K+hCfWjAbWxthVIamjTpWh8a5UhLPBbWXvBlcP9XPrOSEb36IExc0YVtdY86XkVayVhV1efsqQuRLrniddtE/7ddChLbmUdVUQczyqA1qHFvZy19F2jks2COnazGh5GXIeiqV4uGHH2bDhg0899xzY3qv67o89NBDnHLKKbS2ttLc3Jx5rr6+Htd16erq2utztbW1ozpWQ0PlmNI2VlOn7n0YuaYgaJrU1PSvQBkO4bgOU6dWkejzcAlSWxsmpaXADOPqEVC9qJBGpK4a7CAJIEyU2hzH2tfxC20yH78Qxy50vA5V6vwbajTp8Wo9/nbJJ3BtD2XC9Ms24TkpNMNEVdQR1nVc16X1rde47x/OzCwEc8ldTzCtYhY7owPD3SIBjYChUzXCccfj71NM+Y7Xcvt+H5TnHsL0yzfh2Sk0ZWLrFXzqEtANjVCliabnvrpM2C6RQHbDL1esjhTnTYccha6XdhHQcszLscZrru+w17ypr/Drp2s+TvuON9mw6HMfKF+y4qe/ftN0vSx/2zRJW/6MJV7L4bvlitfgftZDCctlWoXOtL63eeDSs/Jev5XD7zWSck6byK8xNcjfeecdbrjhBv74xz8yc+ZMVq5cyZ///GduvfVWDjjgAK655poxJ2D58uVEIhG++tWv8qtf/WrM7x+L9vY+XLcwc0OnTq1iz57evb4mGk+godPVHQPAshwcz2XX7m7CyRiap+jqihNMJcCsxvNCaCqKEdTpjXt4rp9d0bbdWEOONZrjF9JkPv7QY+erAi1kvA5V6vwbar/SEwd/PqUJNpCIAqDsHn55zzLOXrKGSE09se4OfnnPMs654Qf0pCqz5n7GeuL05piTNiF+n718Vj7kM17L7ffOn4H4nDo15H9HB/qSI6+crpTOcc3hzNDokWJV2T3c9w9nUjVlWibWu3fvoLJuGimtdPtIl2OswtjidaTvMJq8Mb0YPXt2cOGKB4h1d/DM/Su57x/OZOGmrThG9RhTnV2/lXM5kbSVJl7L63cfVN9V6fudLjOgc1igg65klLOXrOGZ+1ey/dWXPkA5GlBev1e2YqdNGv+lNaYG+YoVK5gxYwYLFizgqaee4rLLLiMUCrFy5UpOOOGEMR985cqVbN++nXXr1qHrOk1NTbS0tGSe7+joQNd1amtr9/rceGE5tj9kvZ/R/9h2HH8OeTo7nBToAdD8+WYqrINuoGkeGEG8mMwhF2LUNI+T5y7koWXzM3fWL7h5A5rrcsL0UGZ1ZCsuC8SI8uM4LiqR3GesOk6KqinTOH3hiqxYn3/nz4gccIjsGlAA+8obpTR6d7XwyK2XZdU9T919Pa5jyT43QuyDUhq9O9/K2gYyXYa2v/qSlCMxYYxpnMe2bdu4+eab+fSnP83NN9/Mjh07uO+++/arMf7973+fbdu2cc8992Ca/sIMs2fPJpFI8PLLLwPw8MMPc9ppp+3zufEivahbmuofZmM5Fnj2wBxyN4WnB/C0ECgPI6z7jXFAC1biSoNciNHz3EwDBaCjZTsPLZuP5zkk+hIkeuMk+hLSGBdly3HcfcaqUianfXvpsFjfcOXZkOwudpInjb3mTbI7syc5DNQ9p317qb+9ohBi75LdmcY4DJShU+ddQ33zDClHYsIY030ly7IyjedIJEJVVRXTpk0b80Hfeust1q9fz8yZMzn//PMBOPDAA7nnnntYtWoVy5Yty9raDEDX9RGfGy9sx8LQB4aEKF2BB69v38FJtcmBldXdFOgGEAblgqah2+04egNasEJWWReTllIaYWLoroWrB4gT2WfPn+u4mZN5WkfLdjzXJRhSJBO5dwVWSoNkN46ToqctilKV0ss4Ae1PTBVDMKQgoAAN8MBysC03E5NGIIhZ24iHhqaBk1BM/fAhOWNdepHyxzQVRjiI17+unp3uEU9247oWKhDEqK7HA1LtVs78mPrhQyBYM263VyzXMiOKqxhx4DipnGWosr6R+Xc9QbCugVRPJ46VRO/fl1xiUYxHYzpFp1Ip1q5dm/l3IpHI+jfAokWL9vk5hxxyCG+++WbO5z7+8Y/z5JNPjvm58cByh/aQK4jWs+COH/MfSzqZ0tAInovmWqAH8DTdX0VSB8PahRNsALMCL1Ge812EKCSlNMLxHez+0VXYna0DK6mHp+/1BKyrAPXNM7JO6vXNM9ACQQgYBGFYo1wpjdjut7OGyc1fu5lI48Fysp9A9jemCi0YUljK4OX3k5m5ycc3m8R2vMWGRQOLtn19zWbeDc0i5cJx0yMYoYqcsa6rALlvO4mxME2FawZ54b1YJl/+enqY2PbXh+XLzsqDOSwUzJkfKjh+G7DlWmZEcRUrDpQyc5ahqgM+RKvWANvfZMOghSzlPC3GqzENWT/99NPZuXNn5r8vfelLWf/euXNnodI5IViOnZk3DmDoCq1vKgC2nfC3OXP7F/fRDNDC/Y9Bpd73H5phvERfUdMtRDkIE8uc/AHszlZ2/+gqwsRyvl4pnVBlCLPOv5Ne3zwDIHPB/GaiCk1L90IOMWiY3Iyjj+fsJWtIxaOQ7JCtpCaQscZU0QQUL7cks/bd7elo4+l7/cUJF278LWcvWcOv193EQYEegkon6biYtcNjff7azX5vrPjAjHCQ3+2IZfIlqHTsnj0586VJ6+bNRFXu/DDHb36UbZkRRVWMOFBKx6ydmvP8vS1RR7XdxYb+hSzn3/k4F654gJ49LSg7mrc0CFEsY+ohv/3227Esi0DAn7Px8ssv43kDd6GOOeaY/KZugvEb5IMu/j0dkhVURYIEdIeuuE1FINX/lIGHPz3AdhV6agcAmhnBTUplIyYf3bUyJ/80u7MV3bOHvVYpHS0U5MUd/urH0ypmsegnv8NOxHFdh6QWJJHy0DTQNQOl7Ky5n+lhcjOOPn74IllyB37CGEtMFVf2Vlp1IUVlgJyLE0YMOLrepK9zN51OCq1yClf+5HdY8Zg/v1KGcOaN55HJl7qQ4ojGIE6PM2K+NNndhCprWLjpeTRN4XmM+/wo3zIjiqnQcZA+h2/dEachMovLNj6P5ligArzr1NPV52IoWchSTBxj6iF/6KGHuO666zL/nj9/PldffTVXXXUVV1xxBZs3b857AicSy7GyGuR9vQ6apzjh6JmYhkdXn4PmJv0n9YEechcDPdG/wnwgjJeKArIAlZhcXD2AUdeU9TejrglXG35fMRA2M1sRASRsj85dLdwz79Os+OIs7p9/IjMT/4fmeby6K4EWCqLUQHWoDH+Y3Knzrhm+SNais2SRrAliLDFVXP5waBjU8HOcnIsTGjh0b3+djfNP5I7TP8J9806kY1cLoYZmHKNaLkrzSNPI5MuhU0z+0BpHG2HRSMOzeOCSE1l+2kHc/Y2TiXbtGfeNcSjnMiOKqdBxkD6Hmzo09L7NvRefzPIvHsw9F59MQ+/b1AY1bF0WshQTx5ga5Js3b2b+/PmZf5umyW9/+1uee+45Nm3axKOPPpr3BE4klmOj1EBl1dPrz+r7yIENBA2Xzj7b3/IM8DTDX2W9/7HmpTDcTjQz7N+mT8nwMDG5xInQ+LXVmYuAzJw1cuyxrGX3MB4U6OHBxQMrtVZNmUa8vYXY7vc4NNjDX7qTBML+iBSlNKx4L3Nv3UhlfePIi2SJcW9MMVVMlsOc5iCRgJZp+DkjLE7oWCl+ve6mrAvSBxefhRPrQtk9kGxD2T0y1SIP7HiS46ZHiAQ0TN2vY+wR8iXW0zUhp7yUbZkRRVXwOOg/hw89d6frt4MCPfQYtUwZaSFLOyl1nxhXxnQr6/333+ejH/1o5t+zZs3KPP7oRz/KX/7yl/ylbAIaOmS9t8+/qK+rNgkFPNp6LbT0HHLdAPob5IYBKVBWC1rAr+y8RBTNrCxq+oUoJcfxiIen0/itDeiejasZI6/q6vk9jOlGueEOrNSaaxj6vLVPoNUd4b832c26b59G1ZRpzL3lflkkawIbU0wVUTLhEAzBJw800TWdmOVhVOVeIGzP9j9x3JkX8dpvnsj8vWrKNHp378hsuSVTLfIjlXKoCLqc8KEIen9vuaH0nPliBMwJOeWlXMuMKK6Cx0H/OXzwuTuto2U7VbpNjeqhfXdLzvK3+93/Zf1lX5oQZU5MDmPqIY/FYsRiAz2zDz/8cNZz8Xg8fymbgIY2yLt7LDyVRMdvmHf22QOLuukGaAo35aEFdDwUKrUDTH8Yu5eUldbF+KKUhoVN3ElhYWfdtVZKxwkYJJXCCRi4Xu4Tp+N49Dlholo1nU6AvlRy2GcBWPEUxzWHM8NL3f6VWoGcw9DvX3QmVk9b/zH8C4Dtr77ET5bO44KbN8giWRNYOqZ63Cr6nHBeLtqGxnPWdIj+cvD+7k4sbAIBPWe5SCYckr0pPNclEtBQlVOYd+fPsmLxgps38PS6W6isb8w6/mnfXjps/2uZapEfDhrPvBPl9zsSHNsUxtP0YXXEBTdvwLZSI055cZNd2DikRqgPy10hyowYfwoZB04yxdF1HmYgkClbafXNM+h1DaLxBD+/85ph5W/e9x/j6XW3AIPKXKoHJ2AMq29dV6Z/ivIwph7yQw45hK1bt/K5z31u2HMvvPACBx98cN4SNtF4noft2v5WZ/26elIQSOB6/rzxrpiDlh6yrvsL57kJD810cZN16KlWNPMg/3lZaV2MI0pptHR1c8ntG3l/dycHNtZx37UX01xbA2jsTrksfnwbLd1JmmuCrP3KkUwx9KyF1kbzWekLAsdxUYkkJ0wPgaaheSHmr32CDYvOJFJTn/OOO56LcnpwPY8F9/4bT6+7he2vvsRTd1/PuTfcS+PMj6IbBmg6JLtRE2A+qMg/pfRh8bzmnCNoNHXAy4rdzx9/BCu+9jn6envZ0d7Lv/7H63z3q6dTH7BxbQtlmOhJg+ObwyRcD6OumXNvuBczXEGsu4On7r6e3rad1DQ2Z3qJ6ptnyH7khdTfc9eZcPhLZ5KDlMvzP7mbs5esoW7ahwmEwqTiUSLVdYQra3LmQ0dXN7utOFf98/83Yh0mxGSllMb7HV1ccvtGGmuquO17D/HodRdkbSv4Z6uagwI9VDdMw/NcFvzgl+i6oq9jN4loD9tffSnzeR0t29nZ2ct9r3cw/+N1fHPQtcP9181jWrWstSFKb0w95BdddBE333wzzzzzTOaukuu6/OpXv2L58uVcdNFFBUnkRJDqn3Ma6J9D7nkeXd1+g9zDb4R39Nl4jt849/QAnuvgJjz0gIOratCttkFD1ntK8C2EGJt0b2DMTmUaIQDv7+7kkts3knAsUrrO4sffoKXbj/2W7iSLHn2dlK73f4ZOUFcEUQR1RdKxR/yswRzHJdGXINEbJ96XJNI4i4UPvEh988xhd9yP+syZRDt2cfc3TmT5F2fxyK2X8eUrb2PG0cf3N3im49gp7v7Gydz8uQ9x9zdOJLb77XHVqyWKI1c8L378DVK6TsKxMrH78UM+zJWnHs76+Sdzz9/P5vnl5/PdM+bgtL3L3RedyPIvHMRdF53Arv97nXeefocqHSI1DVRMaebH11/EhivPobdtJ3Nv3YidSnHlj17ixl/+mYWbtqKCkZy9SroKlOInmVDSo2+mVejU97zF47f9AyfPXcjvnniAeG8X6y/7Iv90wXHcffGniXa1cdRnzsx6f33zDELhcKYxDiPXYUJMRoPryf9+6z2++/DvOHX5o9zwy3f8c/hBR5JyoY0aPv/tG3n4pm9y25lH8IMFf0sgFOaN//hF1ufVN89AM0xO/+hAYxz8cjfve/cTdyyU0oZdawwe2SREoY3pXvmXvvQldu3axdVXX41lWdTW1tLV1UUgEODyyy/n9NNPL1Q6x72U7Z9o00PWO3uTJFMOenUQrb+HPJoAKxUnCKApPMvCS3ioWhtH1aAl/w+tOgiAl5Ah66K8De7JvvMfLsicBNPe392J7bjoyss0XtJaupPYroepdLyow2/WbyPakaCiPsSxlx824mfl2lI8zXE8UFWYIZ35a3/GhkUD82u//J2VrFvw+ayhpT+54WIWbnwONINA0OT7F/zVsKGnCzdtBaM6D7+WmChsd+R4dh03E7uLzzyex284PyumUh07eOTWy7L+9qPrvsLXlm/hme/9ns9edSy1jdm95E/e+V1623ay8IEX8YINOPhlb/7azWxYdFbW3GWCNSA9QR9IevTNR0O93PVN//ftad/J3FvuZ/1lX8zKu43/eC6Xrt/CjjdfyeTDObf+hG47sF91mBCTgT2ongT477fe47zvvceL664jrEyIpzhhegi7t427Lvm77Olni/+OS9dv4ZV/fyRT5ubeupHqSpMoes5y19LWRSRo0hyu4Tf3/E/mWuNTC2ajKlTOkXpC5NuYB6/NmzePv//7v+cPf/gDnZ2d1NbWcuyxx1JVVVWI9E0Y6R5yQ/ln25+/5u8lHqk5AdwEAElbJx6PUQn+HHI7gZv00JWNZdT2v9+fpy895KLcDb7L3dUX48DGuqyT4YGNdRhKR9c1mmuCWY2Y5poghq5hOGQa4wDRjgTxPYkRP2s0UpZLRW0jZy9ZQ6Smnlh3B/HuztxDfF0PL1iNsnuzXv/M/SvZ/upLMgRYDGPsJZ5dpWdit74yOCzmzHBFzjgMhP3Ydy0P17GwU0nMcEXWa1w7Bf2NOcfxiDQezMJNW3EdS/YjzzPHcSGVyM4rTcu92np3Jws3PofreqAMrtm0hXM/U/WB6jAhJjJjUD2ZNrh8OI6L05cYXgbxy5znecxdvhFN1zM3LS/83r/Skwzn/Nz27j4W3v8ED1xxcda1xgvrt/GZxcfIAq6iKPar9q+srOSkk07ijDPO4OSTT5bG+CikbH9YuqEbeJ7HWzv9xfGM8FR6rPRrNBLxOJ6mA34PuZvwQLfwDH8RKUUvaJr0kIuyN/gu972PP8vqK87jwMY6gMycyZAKYLoua845guYaf/RHeg656brgkjlBpv356RY2XHtxzs8aPY2frVrM3Rf/Dc/cv5JQZTWLHnyB+Xc+zoyjjwcGhvgqpdHbsSvz+p+tWszpC1dw1GfOlCHAYphc8bzmnCMwXZeQCnBff+x29CWHDStPxaM5p1MEq0xOvuJAHKeLVKw3ZywqZWa9z3E8HKPa7zWX/cjzTvUvFJleSb1jx5+z8m7G0cez4N5/I1xdC5qGHpmCHqxl8Xmf55Hf/D5HffiNMdZhQkxMg+tJGPkcrwYt1ppW3zyDaGcb1Y3NGAG/Dq5umIalGTz1v538y5DPXX3Fedz7+LP+tYqZPQUt2pEA6RwXRSJ9O0WS6SHXDXb2QTTq93RrgQDtCT8bkrZOMhkHwwQ8PNvCTXpoGniBMB6g7DYIhEEa5KLMDb7L/d9/eo9V//oLNn73Ig5tNNE8G08ziNoayZRLo6mzce7R2K6HoWs01oRob+vD0BUV9aGsRnmyx+LDdXU8vuJybMfFUHr/iVojVBnELzAeVjw14lAzPVTD/Ds38/QPbuLkuQszQ03TKyQ//5O7Oe2ym/whvsnuzPB28O/AP7RsPpff92sZAiyGcZzh8Wy6biYWm2treHzF5RhK5+C1m7l/0LDymqaDuGDVT3n+/ts4Ze4lTJn+YcxIDf968yJee3ZzZvhl1ZRp/uJg/bF42Q+fwYzUYZiG3/jDJWD3oHsWrmYQTYWxJU7zK1jD/LWb6dnTwkPL5lM1ZRoX3Lwh8/jLV97GT264eNh2Z421VVz4t5+kOhLiwRsvoS+WpKsvRmNtVd5vmhhKo8KMZ7aliqb8XVqG/k1iQ5RKrhi1HY8PNdTy3Pe/1X+tEMAxanA8DQaf14M1mcVa0+XsG3f8FCsVz0xBq2+ewbw1j1FZ28i1px5ACJfHVlxOS1sX7d19rPrXX/Dff3rPb6SnsstBRX3I77aURrkogpI0yFeuXMmWLVvYsWMHTz75JIceeigAp5xyCqZpEgz6d7WuuuoqTjrpJABeeeUVli5dSjKZZPr06dxxxx00NDSUIvn7JTOHXCn+3OnhpuIYSsehnbjdv82NrWGn4hD0ezo8K+X3kAO6YePplejWHrRAGFdWWRdlLn2XOz1s/ZNHHsShdXFSW5fgxXehhQ+g4ribUaHpxBIuynH9EbcO6JpfJmzN41MLZvPCoDnkn1owG8vzCGAMmm+poYWCvLgjTszyV0E+rjmMSiSHNcqV0ki6NpGGaZxz7VruvvjTwxrbCzc9D8F6/wLZyb0PKpqSXkeRk+Nkx/PgIY8aMCVsY+gu9oHTufLHL5GIRXl7ZweXrv93/nr2TJbctArr90vxdu7CCx/A3y++ht72Vt79n5f4yQ0Xc/aSNWy48hzAj0XdMEiZAX63I05QwV/X7MJ5+Ubs/nJW9VfL6VXTpOGVR+lpAaGK6szNkafuvp6zl6xh2qwj+cGCvx225sTlm17ADNfRWFc9bJcIA4VDfvLHUBoVwRTKaif14tKBODh+JXgprBclNkTpGUqjSts5LB5jZhMRtxXrpRsz1wrB427BNaoJBjxSRhXxpNtfBmexcONzdLS8S6y7g0S0m4dv+uaweeULN21FGdVYQFgFCAdNbrr/iUwZ3HDtxTSGazIdAOlrDVuTciGKoyQN8s9+9rN8/etf58ILLxz23F133ZVpoKe5rsvVV1/Nbbfdxpw5c7j33ntZvXo1t912W7GS/IFZ/T3kStfZEwPNilNdFSZh78Ry/fnhrqdw7Sjo/Q1y2840yDUtgauq0OwuNDMiQ9ZF2XMcL9MbaDsu06tsUluvxIvvAsCL7yL1u2WET1xDSlXmvCB0HBdVofjM4mP8u9S630gf2sgOhM1MYxwgZnn8riXOCdND/lyzfkppdESjJHe/w6PXXcCFKx7IPXfctvGcPf4wYMPMbCmVlh7OLnPLxFjkugCt+KvlUNPEQeE6/vkfD+KASgfrxUVZ5STwp7WcufBG1l5yOh0t24nU1Gc+s755Bngef+lKErM8PtGQxPv9jVnvt35/IxUn3EV3PFSS7z1ROY6HMoKZ+mH7qy+x4cpzWPTgCznrlV1t7XgVGgfW1w4b4ZOvm3vpGKO3ndSrd2bFAfFWrCF/k9gQpVJhxrFezFFXnXgnqa03DrlWWErgqMuxX7uH4JybcMwD/TKT7Abgx9dfREfLdhZu/O0+t3wcfG3i4d8kDakAHto+rzWEKJSSrCAyZ84cmpqaRv36bdu2EQwGmTNnDgDnn38+Tz/9dKGSVxDJdA+5ZtAW9dDsONWVIWy7FaX7BT4UDIKTwtP7h6xbKZyE/5ymxfH0CnSrE8wwXlJ6yEX5cxy/Jzus+oep959g07z4LvAsKsz4Xj7DJek6JHFIuk7uE6SmZRrjaTHL84evD5JwLHa1/iWzp2msuyPnHLTd7/4vt37hI9z9jROx4r1cctcTmddlrVgtxBhUmHGsHI3lsIplyonynJzlpLp+CuDHXyoezTyee+tGot1tTFf+TdqQnruc6Z5d6K83KalwLfPWPJZVP1Q1HJC7XumOc8ntG4lZqUx+BzDyOtImHWOaERoWB7n+JrEhSkUf4ZpAG6EO1AJVePFd2C/fRETvI7b7be7+xolsuvo85t66kfrmGSOe04eu95K+Npk+tS5TBkd1rSFEgZTdHPKrrroKz/P4xCc+wXe+8x2qq6tpbW2lubk585r6+npc16Wrq4va2toSpnb0BuaQK9pj4CTjVE2bgm21YuqzAAgGTJRngfL3Gse28Py14Pp7yCvRkm+jmVPw+tpK8TWE2G+eZqCFD8g60WrhA8Bz0fmAF4SeP0x9cKM8EtDQPQii0Aywkl14VpKDplZTNWUaZy9ZQ920D/ONf3qETf94btYWKU/e+V3Av7O+7tun8Z2Hfi8rVosPTPds7FwXmjiEAgae5eFpgZzlpKejrf9m0M9IxaIs3PjbrG3Prtj4HBDB9gyCOd7vamV3up8QnHgXW9Ytz9qF4cVH1g+rV+bd+Tjhmqn886VnoO37GKYhAgAAIABJREFUY/dbOsY8q3dYHHl2ImdsSWyIYlNKH7Gu8zSV+++Wf9PRrzPtzLaOHS3befLO73LuDfcy9SNHMu/Ox7n/ynNky0cxrpRVLfzjH/+YpqYmUqkUK1as4JZbbmH16tV5+/yGhsq8fVYuU6eOvNp8ZI//U0cqInTGXJKJBLVVEZz2PQR1vzESDocJaBYqEMSsDOLqHtH+RSYCpoUWqYMYhKtDxDqiw463t+MXw2Q+fiGOXeh4HWp/voPneiT6Uri2i27ohCpNND335abrhjGPu5nU75Zl5oWZf3UTqXd/SfCQv2dq1f7Hs+d5/PWBEf7r/VjWHPKXH3iDeE+Cwz6nePBaf7/SRQ/8R9aCS0d95kwuXb8FZZhous6mq89j+6svZT67o2U7ViI27K57qZW6vA2V73gtt++XD17Cynmh+VZbCsdLsuORt4lUBzjhvOWZnnQtfACB426hIVXB4h//F45lcce5Hx/+2Y7DtAqdWEc75pFL0F5flXm//dHv0NvWwZQPH4KuF39gXDnm5VjjdaTv0NHSwWu/eYLXfvNE1t8/ftr5XHjrA1Q3NtP23ls8cutl9LbtZO6tG9H6TBpmHIqu62OqQ0eTrnSM2W89hHns1aT+cEcmDrSKZszjbyX10g0Dc3OPvxWtooGplfmPi3LM97RyTlsuY4nXcv1u6XR5rkdXSx//+dMdzPnyMrTXb86KR8INBI+/leSgODWPvRrrjxsAv850XLKGpm9/9SXWX/Ylvvvkm2z5wS2cvWQNlfWNVE+ZRm3TDAxj5OZOrt/rg5bLfCnXvBT5V1YN8vQwdtM0mTt3Lpdeemnm7y0tLZnXdXR0oOv6mHvH29v7/L1AC2Dq1Cr27Bl5Xvfudn+ey54ucCx/TmvQNPG8JEHd7wY3VYCAbuN4ikRfkmRfHFzwPAPHimFZtUQAT3m4iWjW8fZ1/EKbzMcfeux8VaCFjNeh9uf3U0rHizrDFlzTKtSIQ70ioemET1wDngWeS+rdX2IceCpdMRO794PFs1I6J0wP+atMe/DyA2+w49U2Tr7sIHo6/8iFKx4g1t2Brgx+9N2vZk7mr/3mCXa8+QoLH9iKh0Zv286sz61vngHKLGl8D5XPeC/HeC11fVIohjKp+qvsxnbsqGXc9u9ttEVbWfOFj/DfP3iNF4FPfvVuNCxcDHpTYX9xIQ8MrdcfFj1lGqfOu4ZITT2peJRAuJKPur3c9bXTuHD5Rsyp84lUVtHT0cYT1y2kZ89OFm7aimNUF/U7l2OswtjidW/fQWlGzjUmejt20TDjMH7wzc/S0bKdGUcfz9lL1vg3/jyP3rY9OEblmOvQfaVrcIxZf9xA4Ogr0Sun4xCkJ73K+gl3ZVa17k6Fsduio/odxqKcy3Cx0laKeC3X331wuoK64vl1rxHtSBDvqeLY079HpEYRrInQnQpht8cx1AFUnHAXChtwsbb9ALfzj2jhAzA+sYyW7e9nyt2Mo4/n1HnXUFnfiOY69LTvzCx6Wd88Y6/1Xq7fa3+ubQqh2Hkpjf/SKpsGeSwWw3Ecqqqq8DyPX/ziFxx++OEAzJ49m0Qiwcsvv8ycOXN4+OGHOe20/5+98w6Qqjrf/+fcOjM7s51tIEVEAwEUI6BoNJZEothQk0hUROyKCipgBBcElaao2BWxRE2iwUb8qV9LNEoEVIyosUZQYJdts212Zm47vz/u7sCwg5WmzPPP7sy9c8897T3nPe/7Pu/wHfzG3w0dLuutjo6X9ONlcwI+m3xA9XOSB80AAdVFCj/WRToWKAqgI0QCqfgnpIrugWuDkwTN3M41ySILH5oUvNK+YIGfs/P1u97n0PH7bJHsrC3hYalhP82JcFB7nUTSCqFK/6Dp6xY7VRUEaUPxbDxFJ04ozW3cdb0UgZuJyrr36ijsGUYqUR6beUHKfW3MjY+n0kZ1wCdys3HDJZw463H+Pvmk1P0nznocN1gASfuHN1oWuzQcV9KilhEctoD6lhh1bZIbXmjgvfU+J4iao1PUM8yeB0Tw4klQdWIE08a5NHI5787naa5b3zmtVn6xP64VhRtOP6xT+ZsSG2WxldCe/mzhJunrxsxfTL1aSGPCTikMI8Zdy6OVYzf2101PkFe2Fy+3y9CinmH2PrIYzW4gQIiYGvpeYTEdY2xTpbslldrMf146gVvWjTeL7QyP1L6hfnULL97qK50jpu+P0757kIBrSaTnIfUA6s8vQvnZ2VhJmydvuZ6qzz5IpSg9eNS4tLl1yvSFLFlwFWveW9aJ0K0DqqqgSUGsIY6pqGkEbt9nb5NFFj8UO2RpnjlzJi+88AJ1dXWMGTOG/Px87rzzTsaNG4frunieR+/evamsrARAURTmzJlDZWVlWtqzHxM6WNYTjopn+Qp5IGigCIWA2q6gB01CmkfSU1ABadsITUfKDoU8hESg6u0CK9mCyCrkWewobLKodsDM1Yl5FrbrbpE92HElrVYo4wm0uoUTaFUVBOPrqHnocpxoFVpBOSWnzSMe7Jp506r4OUT3/FUe940/Mi0FyqIJJ3HylNu564KjU7d3kL44CBZ+bjJyzgsUGJKoJVj4aZKr+ijZbWsWWwWOK2l0cjj7ic9Z35RkQHmYG47pTUFQJRyGYSeGaXjsEtZuYZy7rkQPhlPKOGxMqzXu/tfSiI2ymQG2PTrSn130wFJqmlqpS8CflreyqupL7j6mgsKKHhxx5qSUwgDt/XXpCVx0/+v87NSeGCgU6w00PHYJsW8j374BjiuzSncWOy/a1+dN9w+b5vzOtN6XnjaXP8+5klUvP5n6zWuPLGDklTez4IzOqUs7UkNmknsdFvBX7nofM1en1/AKgl0C6JqKqWrgdt7bxBoS2XzkWWxT7BCFfMqUKUyZMqXT908++WSGu33su+++PPPMM9vytbYJPMej/n/NOPX+TG6zVDzLt4gHTB1FqAQUXyEPGiY5ukedrRABpGMjNM1XyEmCUJBKDqrwXdxlIobIKd4h9coii80X1cKeEcqOLmHKvYs5+dDBFOWFKSmIkB8MohiG70qOh+40o0ibpoRF6V557P6LcoIRgZWIYka60JZBYwjSllqcAZxoFTUPXU7JOQtpJdjp/o785fGW6owpULp075NSWDYlfTE8j3MP6sn4xR+yvilJRZ7J/JH9KMzRqY8nt34bZrFLwvA8bhrZjzteX8MZg4poaGzExsDIsWl4bNI3jnPXsTKn9vE8zr/zObxEA5P/8k82rP6Mv98wheba6q1GbKSpwvdwabe+xlLW110XritRjTxEXoTrXtooO0pKyxh781NY8daM/VVdV8+RUx/kmWmnozw5JdXvaqQIkagnLy+Cp5i02QEsa+toA9n+y2JHQ2qS307sgxNPoJgmCS+MZuq4jg2Wv943vnQ3JSdchhrOw21tIvrSPYy66mbmfrQytW4fecE0PFdmnFuhvMItErp1WMDNXJ3So0oYfcuiVD7yeyePoUdBwdceGGSRxbZA1nltG2PN0mo+e2kdOWoeQSVIm6OgOAlMQ0dRFBShYioJXCnQdBWAmNWukCeTCMNESgOFOAiBVIIoXjv1enLnixPKYteAqioIBIdevA8tNW28/+xqeg4vZ87TzzPm6F9y+a1/TVvgWrQ8kq5k/7wNuG/5OZhDwVIGj5iO/fFCvE+XEgqWohfPwFLL2svY6KIupExtVjvgRKu2mK6nI395jh7OaCnUzJwtsKZLSgyFRaMG4ngSTREYnoeySfo0VRWQbMJ1LT9PeZZxPYvvCNf16BbUmfTL3fiippY/3fV31tZEWTrv7K8d5x1jD0VhwGHH86uTzySSX0xLYx3/fOw+jGCIUI6DvXwB7icb6BIsZdyCB4k5ebhqzg8ep5nyqEcGz6BFLdvllTrX9SgxFB45dSAyVovr2KhOA1bhbqiJxoz9VdPkH8bnBbVUv5vd+1N6wjic9+eRfL89V/2QmWCU/WClPNt/WexoaKogx6vCXjEV4huQwVLM/Wbwn4Yy9ioKoLpJFCEpPmIUzvvzUuO0+IjL8YwA5y58jfpolJqmONOeeJtrT/t1ilPj2POvIpJfTDzWQkH3PRh3/2uowSIs20tbt13VxMzV6H5EOWfecj9ra6IArK2JctasRTxx7UUcdG7/Th58UvXD4bJ5yrPYFtghech3FUhPsu4dPz2Z4ir80h1KzFJQXIvcnACO56EpGpoSx3I1hOp3R1O7IU5aCYRhAjqQaLeQB31rOSATWYU8i+2PDnevl29cyZJpb/LWXz5h8Cl7ESw2OfnQwSllHDYucAWaTf/8BPKt9BzM1opKtB7DU5/tFVPJMeJ4jkNYNkLzeuzqT3AbN6AVlKe9h1ZQDppBIBxAVTuLMtf1kEYuY29+Mj2H+C1PoYXzcbVcpFmEq+V2ikVXbQfTdVFtJ23BNXQFL7aBxurVVH38Louvv5C2ms/8xT6LLL4DFCGwPYcJC/6Smi9V0VjGcS4VDVUVtNV8xuLrLyQWreGPl1+H/vrdtD40Dv31u/nj5LnkGB728s3znFcSDCpb5dBoS3nUc4z4D372jxmqKlCdZry2WtymdSy+/mJmHrUHC844GKv2C/71cTWnXHZtWn+dctm1PLtyNQC1zYlUvxcdcQbO+/PS23j5FEJ6YkvFf2tk+y+LHY1MY5C3prJHOM7y9XH0oIGm02kOOO/PQ9M8quqiXPnQPzlt3mM8v+xDrnro/zj3zucYM/Wm1PwSryxATzTyr0cX0LLhM4IhNZWzfOZvd+eWM4ax169VQgVGSvZ2YG1NFNt1ETkqh47fhxHT9+fQ8fugRTScFodX5r/Lkso3eWX+u8iYm3HvkUUW3wfZkbQN0VwVIx5Nstfw7rTmtjJQ9qPVAmknCecEcD0PXTHQacP2NGR7dzTG/c29tJIohh9DDklEh4Vc+guzTDTvqKplsQtDkyJ1cgx+bNW/7lqF4ikU5YUzLnCK9AgoTlq6J2jPJ6pH0j4rONg1n1N1z/msv+Ms6pfcCIZJye+vSW1atYJySk6dy78bTJauSyAC5haUckletz0Z99C/mfiPLxh97xt8FeyNpWiYATXt3o5NNck6VKe5k5KtqoKWqk+47azDufn0g3hizngOHjWO526f5lsts8jiOyAQNpCCtPly7WOvYxyfPs4LT5qNI8KQbOLtfzzMr8+5Crc1Ss3DE9NcnGlrxG2pyTjHtuRJ8l2hyMxzeGs9/8eIjoOSBWccyIzf9uK2s47g4FHj6DFwaDtnxYmM2KcHdY+khyLUPTKJk/bvA8Djyz6l5NQ5aAXlqOG8LbTxDyeVzPZfFjsaWxqDAcWhzZYoQiCFzHiP11pP4XNTuOvMg9l3z+4APL/sQ/LyCmh+8tq0+VX7yGQO/8P5PHdbJVZzXYp0EXyX9gcnn0hQTdCtpCCtnG4lBeiqz2WT9FySuCQ9F+HSad/z+l3vo8nsYXwWWwdZhXwbonldO3t6oUlTuIkyWUJLwk97lhMMIKWfokRXEiQ9DbvdgBGN+/90uKyDjkCCcPCUIEImQIDX1riDapbFLo0MZG6xhgQBV6VLXiTjAhcwVBRVRwRL066JYCnSbkn7DArVD/rx4mb3/hSNmIBAoOWVUn7+Qiou/SviN5fRrIaJJiRttkydrG+KDgU72VRDq+XxiVPEytYI1TGPt9YnQd+okBu6Qqzm89QJ+oIzDuxs+U42sfDSEzqRxww5brTP4prFdoOqCsJqnFzRTFiNbzMPhW1ZjlAEpqGlzZd3PvmSqU+spOSceymf8BRFY++lnjKkJ0BI9j/xLO6/7GSCOZE01/b8Q0ZT89h03OaGjHPME1snOs0T2jZ9/o8SVlOnzf6jlWM54sxJqc8KbsZQhO5d/FRMow7qS+NL91I0YgJKTlFmOanoP9gat7X7b3vNw++DnfnddjZsrbb6Ns/Z0hjUNYNf9QwRs11smfkep6kWJ1qF9eTVXHXyQezbpzt//dMpKJ6VcX7Fm+sZctxoXMfOGGeuKR73ThqTksHdSgq44+LTCKvpewn/xbNEb1lsW2QV8m2Iluo29KCKFlCIhhuRQLMFVjJBKOBPeF3R0YSDIzWSjs9mVRfzFXLPSiJ0o91CDopIIoVP7KMYGjJWv/0rlcUuD0UTHHLBQA4fP4hfnjOAop655BQGcJIeeaEc7pmcvsDdM3kMN766lnP//iWJvaelFloRLMUYMh1nzXOpz/rgGX5Gv3ZlvPDIC2h5ZwmyrRaZrEORCWLvLCESCuC2pwcEaLMlbBbnnbJaDe/ForEH0jPxOfmmSN2voGIqKrqhkog3ct8lx3Vird7U8u26mYm0woUlfhx6FtsFKQbeu8eydu5x1Nw9lmB83VbfcG+PciKhEHe1bwj33bM7z143mlsvPBIMlateb+DAez/m/Bc+Zb3lgPSIRetoWL+Glsa6NNd2NZSLE62i/sX70fpfnjbH9MEziFmdiQ+/D2JWEH3wjG32/B8jPDu5RVIp8MNkPKFmDEXIDZosvWUyfbsV0/bhq2x46AqqH766Ux+y3wzeqjO36An0bbE1+297zcPvg639bpoqyAsmKAi0khdMoO0Eddwa6KhXntaIiH5G7V+v+t5t9W3bPOMYHDKTT1oCrI3GGf3Qu5yTYa+g9b+c+hfvB/z9QbfCMNf/YQgvTj2Jrz56N+P8aqqrJlxYgi3VVNhaBworeqBoBnX/V8d9553BS7Mu46FLxlIRyAXXz5WeNtfaSWw3RYroLYsstgJ24WPtbY/mqhiR0hBSQpPeRFRJ4DgBHNsh2K6Qa4qBrri4Uidh+Va2upjkredfYnfbIpZIEmxXyFEspOILBDUcxmtt2CH1ymLXhaoqWK02b/3lk41kJ2f3x+wSRApBzJUkjXzun3ah76ZuqNz46loKAirTDy7FNHX0g25CSBeEgqcE0QaOB+9CPPx8uQHa0ArKyT9kNE1L/9qJ3KVg/0pq/nEPgd/6FqiCgGBgQYKQ24YZVIlZQWSys9XqwfHHM/reN1iZjBDSBY1ftvD2g/9l6IRBRJtjRIrLOGHifEJ5hbQ1NfDifbPxXBvVFDTXVaMqSkaCuNzisq3CXp3Ft8N3ZdzfGctRVYWE44GiIEL5PDvzLHL1ZuwVlXif+uN89qHT+XBAd+YtbeDSxR/y4HGltDbUUFjRg6fvuJZzr7uX5lcXkT/0GIyiYrqecwt1z93NhicWUHz0eIzCMoRq4GDA1yQI+C6s25lyXMfcEAE3hqLYCEVFIvCkIM73y6P9Y4KqClDVlFzoMXAoR5w5iXBhCXnFZVz5tzcorOiGq+iUjrmFDYsuTqVxCh97JY9edwknXnU7im6iFZSnxpprgXHAXIRQiHk6tiPoazZhx9oIhvNxLJsctRWvtZmCoEbMDX8rwrevz1H+3bC95uH3wdZ8t58qEV6mepWecDkbnljwvdrq27S5qipoQYOk6IFx0K0IzwZV5606k8KAwiWL32d9U5L1TUlueSvIxF/OJ6B6SKGBY1F64hU4TbU0LnuGooII+SGNi257irbWZiInXE3TE9ek5lfeCVcTbUsSKS7DE3Dhotd4ctbFrHrlqRT7uhLIY+8REV67cxWBXIN9ju/Nm/f/N2Mq1o7MLZsTvTnixzsGsti5kFXItxGkJ2ndEKfbvsUgIenZfKlH8Wz/1Dxg+kq2rhroqocndex2C3l9dS0vL/0Lux82gOUvv8GvB/4O0wQhknhKu2ALBXFiWYU8i+2LjnQhHa5bgVwDN2zwxvoEbbYkpAsGlQf5sAaiCZcBXTQKAioT94HG524kNHwsWBbJlbP9+PH203Er1AM7buG4LnE1RNnoG/CSbeQPPaYTuYv19nTyh54LwqJ7JMTPQ9XI5VNJbrpZUgIZrVaGtPlFpIUgNvEGh5K98rCsBEV5EY659PpUbufCih6MmrkIIxShueYzFl5yPJHiMkbNXJR2z9ibnkCPlGHZWb+17QXFs78T4/7OVo6qKoiAyaurYxQHFEpbP6M1XkuwdmHaOHffqqSwy1hmDi7ECZagKgrLn3qAU6Yv5NHKsbS6CsVHnYXzzjUkP2rfUI+cSPSNZ1BNHevNSak5FhkygyhdiFsOmqoQUHVcV34vZWPTHNe+VWxtWr7gLidNpemNv5J/xDnEg11/UFvtzPC9cD7nudsrOWX6Ql57ZAEHjxrHo5Vjye1SxlnXLSD01QKcT/x21QZfgzJ8ImFNo6WxjgeuncCa95Zx1GXz+TgZYe/Tb6DxxbtSB5Ad8iw4eDqtS+6i7YNXfUX+nDsJiBjWsspU/4YHT6fV6PqtlfKtkaN8e83D74Ot+W45Rhx7aQYivGG3bNaOPy5kqpfz/jyKjjiX9fdd/p3b6pvaXEqJCJgsXRdv3ysYDKnIQxNQ1dpGcVBhfTuj8cCKMJcOCWAkq7E+fxy990islXNT471kxDTqX76X+Gcr6HLSVKz/PAm/PB3xm8uo6NqT2nWraXHgwcmnpq3nIy6dxRETbiYc0NEDeVi2R3FFmMMuH0Qbglirxb4X741nuXhxh3hzLQGhoqo6qHkporcsy3oW2wJZZ4tthESzhXQlZq5vCU84SaqNGJ7tCxzD8M9COizkCA2N9jjUL/5DOMdXvFta2/jk3c8AXyGXHQp50ERmY8iz2N7YLI7q58f3ZmXU9l3G8V3BV1bF2bO4IwZLMH5oHjV/nkj+0GMQXgtWuzIOIAKFkKgjYNWQo8eJGBY5XhNqTiFqXhlqbmFGchc1t5ANn3/Iz3OinZjb7RVTyQlpGV3UDOGyaOyBzPxtLxZdcQQVv7CYvmgxq9dVpxRt8JX3R6aMwUnGUpb2Ne8t45mbruTkKbcz9dnPGXf/G4RK+2SV8e0MT9Ezuidu7TjmbVWOHjRYvt7flBbJRhZechyhcCTjOA+FIzwwYSRtX7zL4usv5jfnTuW1RxZwwsT5FBTl47xzTfqGetUcin97Ds6qOZuxdE+lKVpF1VercZo2oLktGIaSkfHY+fh+cs3Yt3LPzWQVq318BpFfjKDmocsJ0vaD2mqnhtXMwkuOY9UrT7FkwVWMuOQ6Hq0cS8P6NRw3biraRzduJpeuRsok150yjAXnH8ua95ZRWNEDM2BiKIKkkU/JyCs6M6yvqKTLiIswu/f3Dz2Eg7XCV8aVgr7oAy4EzyKitW5XV+rtNQ+/D7bmu/1UifC2VC81nPe92uqb2jzpypTcA1LcL4oiCOm+V82ZQ8tZen4fHjixjEhAxf78cbQew1PKeMc7Wm9NIzLg4DR50/S3KXi2RVNrG4Girtw34eRO63nDuv+hqxBrC+G0uBi6SqLNYn3C4YxH/sMJD73LOY+/T4uQ2InVLJxwODN+2yvFKQMyjegtq4xnsTWRVci3EeLRdsU75Asjy7VoMCxkSiFvt5ArOoYiQeiEFP+aVrua3ffqDYCZG2H5yysAECKxUSEPaHhZC3kW2xubxVEZESO1wHagzZYY7YtsgSYIKB5OtAo1nIfQAqmFVSnoi953LPZ7N5F46VTs1y9C9+rx4o3YtWsAiboFgiMCeSy+5WraGjdk3iwJOPPmp9LTnd30BE/OvSxtkb5//Amc+sufY6pktKjbdnrc+Jr3lnHXBUeDUDqlS8ti+yBOiJLT5qUz7p82jzihb/ytqiqYiorZzh/wdfG4P6ScLZXt6hrNSYdeeRr5AQXN88dXc0NdxnHe3FCXikde9cpTvHDXDI66/CaUin6wBSZipJvx+x7FOZS9NA37nlOJLjwHo3UtCumbcqWgr2+NeuNSrJf/iLv0YiKieouK3pasYh0x7T92peXr4DkbZcOa95bRGq1Nfc4tLM7YB6U990jJpAGHHc8Vf36dsCHox1qa7h2L21SVmV062ULhkRdgdu+PUMVGZbzvWOxVt5F8/WKSb1xKrlJFjvn143prYWvPj62JrfluP1Uiwy3WKxH/Xm31TW3uSTLuFfAkQyqCBDWPiwZJlOUTSLx0Ksl/T0LvPRJhZj6U1/K6UHraXEp+Nw2jdHfUSBFlvfZCz8kDRc3sIRfMobGxES3oIQCZkNTHHdocjysO782Aigjrm5LEorU8evnIr+WUySKLrY0ft0TZiRFv8JVrPdRxOmjRooPX4lsXTUNH4rusm6pECJWgYgGgxJup6FYGrRvoulcvVj39T2BPBAkQGlLoqKaKjDf7wWaZGCGzyGIbYPM4KifuENKVtIU2pAtMAT/3HN66+2MOGVOBVlCO29qEogZ9xuD4BrQ+p2B/vhh9wIUIPYK0W7A/fhD9Z+ew7s7JqJEiSkZegTlsHkgXz4lDshEZKOHx+dew5r1lNNZsoLz9eR0QwVKk6xLIL+aCe15E1XS0QBjHirPqlafS6tOwfg2FYZOG1mTG+HBPaBm/d1UdXVWyJ+Q7AK4riQe7UnLOwlQc7LeJV1ZVBRlzUyEXm8cIbq1ytlR2jeUxvj1GsiLPZM7x/VAwKazowbJnF3PShNkoTjPSasRZ8xxW2QiemnIphRU9aGvyD19XvfIUx068iVBxBapoxSsdhtZjeGr+OGueQzoOSobvvebaNEt244t3U3LChNR8BND6nNLJGmWvmEpk2E24CQdP0dPaoMMqtqlSrhWU47Y17zTW0m0FVTPSZENbU0Pqc3NDHTkZ5FLd2jWcMHE+pb32org4DzdWj2IUUPvS3TjRKtzWplR/KAV90fqcgjDyEYFi3EQVBUdeiHSl7wKfoa+s5VejD5mHZeWCsW3l09acHzvbu23KqyDR0IfOxl62MQREHzyDFivI93X33xkQs4JEhs6GeJV/UO4kIFSGI8PEXfM79+M3tbki/L1Bmy0pCAj65ycIqg6mZ6MQJM9oQSYs9AEX4nz6KF70v1gr52IeMCtNRoE/l0QgQv2SyalQmZKTK3G1AKf+ZTWLju+Wcd224jFqEiZmyCaSa7Km1Wbi//s4JZOnHbUnC15dTY7qZlToPdfOak1dJypQAAAgAElEQVRZbDNkLeTbCPFoEiHAyPEt4UnHwlJ1hJVAAEZ7yiVdMTBVkFIhpFg4ri/YcnL93My79dsDKQW2BYj2/ONKEEUXgMRrqd0BtctiV4XremgRjcMnDOKY6fuTV2QytGuQkO5b0EK6YFC+xtJb3+W1m1ZSv7qZz1a0UXrqHBqXPYNUIhiDJvkn86Gu6Hud6lt43hiPveo29L1OReg6pafPpnzUlbirZpJ46TSS/56EcBM4q59BeFbqfVa98SrGkGs6sbHWPXUjds1qZh7dhwVjDqGpeg2aHsjoxt7QmmT+U8s4/aanueSBf3HVMx/zp6f/y/n3vgRmbidL+4mzHmfWGw1YSlZ87ii4rqTVDdLsRWh1g99q86hJ8Z3zyH6fcjLBUhTGL/4wFSO5vinJxCc/RM0p5PQFzzJi7Hk4yyaRfP1ifx787HTe/L8Xaa6t5pTpC/ngtSVMfOSfzHt9NcUFAbqHkthJG32v09Lnz89Ox3aE/3fVbdgf3g1Cx+h/HlpeCWb3/gCY3fvT5ZiL8BLNGPvPRikdBoAw8jNbaFs2ZGROzmQV63LSVFreXrLTWEu3FbxgAb+b8/eUbFj+1AOMufFxCit68NSCGTDwyjS5JAdM5rXHH6SopJAuJblg1aJoLsJponTkBLpe8iDxdZ9h/KISpXTYZtbv8WgBP9+8owQwBk/fYl9hNWPV12+X/Mhba35sC3zfd+vgVXCXXoz18h9xll4M0kI98DaMwx5GHXYLLfLHTeiWgrSw37vJlx/v3QSe/b2U8Q58XZubqmBoRZDysML++RvIWXEx4pU/Yr9+EYa1FmvVrSRfvxhnzXMY+/6JwOEPYg6bg1SDmAfMwRh6PUpBXz87y36V1D59czqB3GPTEUjWNyWZvbSh07o9auYijMKu/PlfH5ATMYlrSkoZB18mT3v2E87cfzdibmZWdqEZ28X7JItdE9mznm2EtmiSQJ6ROj9NuhboOahOlJBqpFIXaqqvkDsIwmqShC2Jqzm+f4+iEMqL0LVXV2ItFpGidoVcBFBU/wmyuRryf7rEOVnsXFBVBZnwaKpLoJkqTtIlUhbkoG4BWtri2LZNc10MuYnloKSHRst//kHR0ZciAiYIgXHQLQjPJrnsT+kWnhXT0QdNxMjtQnLpFenX3pmFsc/lWG9N44RL5vCbMy6ioLgEKUEfOheZbEEYOTS8eD9tH7xKt+Hj2H/kmfz84BHYyTiOFeO8O5/jzvOGp4hextz0BNOffAeAgGHQ2mpzx7m/SV3/w6y/UtyrL4fNeYECQxK1BLNWtrKqKsqEwwFdw/C8rKV8J4GqCoK0oXh2mjVXVQUx1+Jnp/bEbfP48sUqGla3bLc8so4nUxu/DqxvSmJ5EjsQQftoxmaWzkoO+/089jtmNP965BaOPfsClA/nYf9vA06wFGPQJPRgF6yl0zr9zjxwPtaquxGBQvS+Y9PIkEpPmkT0X09TMOxo7Lf+lPre2K8Sd/dTkUogozXKbfYt9JszJ29uFROKghQK+cdM2mmspdsKcQ+uXSk4d84L7J6vUdPYijBNTp5yO4XlPWlug2SXsYTCERzXIz9Yzu8unYS1opLE/9rbfdAk7A/uQCYaMIZMI/cXhxN942nyDzgLa8WVnfgBRO9xKHoP4npXQkprZsuhkUMgV0mNa1UVJFwbISBPsVClgyf0n3z/fF9kJHFbPhV12C1EE+H2u3787balem4LsjpVFdQ2tuK5DvsVWJ3KtZZX+pZxK4reeyTJpZdvlE2DJmH99572OXINjiVxZYi2D15NK8OJVqF5FgPLw7z8aZRJvx7MuAeX4lpxXAlr62M8v+ILpo4+jtamBGZEUhTS0+Ty+qYkBTk6uujCKfMWp9zWOw7iZ75ez7kHRSjZxt4nWeya2CEK+ezZs3n++edZt24dzzzzDHvuuScAX3zxBZMnT6axsZH8/Hxmz55Nz549v/HazohEY5Jgvon0fMGdcJN4egjsaiLCTKVIMtQAAQ3qXcjTklhJl2ajGLctDqYvFPv034PWpmYCOT6Jm1QCKF4MAK+pCnUH1C+LXROGUIg1J9LSnh141s/Z0NzI2FmLWFsTpVtJAXdechr8A5LNNnklOrX/XIkYdBD22z5hkTH0etBzMlp4RKgMpIux72Tf3bbdfU3GNyByytGHzUNRNHSzHLwkWFFQQrgEUDyF8NATiX+2AqdhLcecexU3jDlsIyv6zU9y6UPLsK04imZAqIDpZ/XCdFqp/ew9Hpt5QVrc2F8m/57zF/2L696Ipi3cFXkmn9bEmPvS58wf2S+7QO8ESOXB3YTxu+S0eVjhbtS1tNJcV01eUKNNceg1sit5/47Se98cTKURfSu622ZKI+Yqgoo8k/VNSQaWh7l0/wJKQ4IiWokF6TQPRKAQISR5YZVjzruM5Ovj0zewK2djDpvnK92bhHw4nz6KTNSh9x4JUnZyaXb+M5uiX8/Gane/7XCLRjooepjGfz9FwdBKrLenpzbEWn8/FVIHnGgVqrRRVb+9XFduTI+UNgV+/ErL10ETUN9mc9Gz1dxzUh9GVt7Gvn26M/64oZQGQ9x65q9SqdBGX3UjSqIW672ZnfrRGDyN5GvnYy2fhr7vn8g/4DgUTU1z3e24Xw0EcZtrUXN1msghd8g1WMuvTuur2qdvpuDoSQjRiqlI8Dy8RBvhnBAyuh5PgGfFCRV2pU3rklXKN4MiHZyfIInb5the9VRVQbQthpZsprwgjIKXmfcl3ANj38lY79+VHsb2+eMYv5gC0sFLNKIGi0ms+ShjqIxd9yWX7t+VKS/bfFgdY+5LX3HbST+nrrmVUGEuIw4q5A/T7kztU+aPP43blsGqqlbAX9dLwwaqADX0My56cCk1ja3UJUgdxH9U08aiUQNRs+t9FlsZO8T34vDDD+fhhx+ma9d0y25lZSWjRo3i+eefZ9SoUVx99dXf6trOiESzhRHWU58tx8YTQTw3QUQxMWN+05uqSUD1GSgjaoJkIkmTUey7tgcCID326N+beMwm0eZbKaQSQMiEn8e5cf0OqV8Wuyg8ePPB/6alPUsGvJQyDrC2Jsp5Nz/EwNP3YN+T+pBIKJSdOiONPRgzH2FEMA+6BWPINSgFfQF811m7leTSKza64fY7J+WqhvRQAKv+Q2irxlo5B+ud2QiZQHgJ7JrVKEKh/Px7EaqOlYynNsUnTJyPFY/hukn0cBdqiPDHP7/H5Ge/wLZtjGBOxrgx17GZdtSeVOSZAKlYs/ve/Ir1TUnGL/4w676+FaGpAplo+FYs35tiS3lwDeKE26opfG4K8r7RFD43hSJRzz6/DhF/Zhxr5xybcsU2DIW8YCJj2aoqCKtxckUzYTWecttOe4eAQq5SlXJ37SBFCwqP+SP7MWZIOfccW86AYpUSU0LDlxQE0smVlIK+aP3HIWPrIFELrpVxAysVA73v2eku6wMuRmpBrJVzEcHMxGJCVTOSgtkrrqTggN9ii1yMA27APPRBjGHzaVr5Oskv39/YPwXl4DmEaCSifX17/JShKoJrj/kZFXkmzUmPbiUFvPPpl5w27zFs207JkmPPvwrrrb+jhfMy94eeg7LXGETxIIQWwF5xJYmXR/v92XdsSjaKYCnSk2i5JWjCRVEFnsiDHueiHXA7+uDrQQ/T5YTL0U0P3bDRVI+GJfNIPD0DGV1HzWPTWH/XudQ9ORvZUk9I/ZoE9bsofqokbptjW9ZTUwV5wQSFwRbyjFZ2C0vyqt5GpQmvdW1molYkUhjovUd2CmPznATJpRMR0gVpYW34nC4nTU0LlSk9dQ6JtR9RGhJp6/OFj39AUX6EHFPj3Dn3p+1Txs9/iLOHlgEwekgFC0cNxHElnoRPlqwmGs/hD4uruejZ6pTSvr4piePKrOt6FlsdO0TC7Lfffp2+q6+v58MPP2TRokUAjBgxghkzZtDQ0ICUcovXCgsLt+u7fxtIT2K12ilCN/At5IY0cewkEbWAUJtGa8jB0AIYKrTZHgWyGTvp0Gx0QbfbEAX5SCnJieTQGNWRbgyEQIoAuG2IUD5eU9XXvEkWWWxdSE8Sa0hQ1DOXvY/vjdktQl1jU2qR60BJfoRcJYEo8QjkqgiUlMWHkv0RQiH578mbuKVdgf35YoyBF3W2Br4zC2O/q0EoWO9cn3Jds796ySc2Wn411tvXYx40Hy2Ug127FoJhav8+k9JT5zJ9yYdouslfZl/GqpefpLCiB2fe/BR3va9QlGMw7pCefNVcTyQey0gEs75VsmDFaq44vDd9SnKoboyjxBuYPCRE1MrhrpWtOJ7MeqpsBXTEbyZem7oxh/Y35MXuwBYZv/EIey3k/m4ablszja8+QOyxPxE8flI6ydlLd1Ny7HlYS6/ulJNbUQU5ohEvVg8e6JFiTKUJKTRiTpik5WEYCgHZmLJYwkZStOCwWygPhjhvUBDFtXBjUdzWKC1vL6HgiLP98fzRA2g9hqOEe4AA65M/421YijH0+syuyV6S5CYpBP2Qj2mYw25AHzQRqZgZf4dQt0wKtqISfchs1t52YcrLoPS0ueTscyQkY3hWHDW/DDfeghrMxa7/hOhL9+K21Psx48Guu4zF1ZUSXYXrRuxJud7Gk1NG8Xl1A7MfewMXwWFjruA3p5+LGQjAoP2Qyca0/lAK+qLteTpIid5jOMK1Uq660GFBn4s+4ELsVbdhDJqEDJbh1H2JkpNPJOTzyLihAoRMYK+Y3h6mcHYqtaQIllJ85CTsFofG1x+laMQE1FCuPw9ef5SiYy4HsqSwmyJmBYkMnpFKB/hTIXHbHNuqnpoqiCjV2EunpuSovs+V5A46hOQbExCBQoxBV6SF0hiDp2F9cDfGz88m2UkmTfe9gYoHId0kihDkHzACz5OUn78Q7AROtIroS/dS9NuLyNFDBOPNzD80glALWfw/l+aEgx1PdNqnrK2J0qsgwIsXDKGuzWbsI++lCN5uOKEfAcdLeTZ1oCLPJF4TJ7ckiPu9WymLLDpjpznyq6qqorS0FFX1t7WqqlJSUkJVVRVSyi1e2xkVcivmIL2NKc8ALBdUqWIlE4TDJqFWFYohoBoYqiBuu3T1WklaLq2BYgLupwjTTInFQDCCEbCpraqiIi+AQKJECvCaNmR+iSyy2BZQBV0HFrHXr3ZDCeksXx+nR0ihW0kBJfkRZp31G3pXFKELD6TrK9GrbsXbsNRnft6vEsWzsb54BvOAWfhOOh7W6v+HPvAS8DJbA4WRh9X0hc86rEeQiQb03Y+FeG3qHhmvI/n6xYhgKeqQayj9wzSiL99DZN8R1C+5kRPOvZLmuirWvLeM+y45jlPnv0SP4gjCaUYUGVih3Rk1c1EqH3lhRQ9OveEJKpc3saqqlbkvfc4DfxxIuPkL/jbxxNQ9V835OwFVILOr8w9GxrjGFd8urjET43eo3yF4rkWgbDc/vtkrRjtxCnV/n4liBDG796foiDNQw3kooQKs5Vd2Kjs87CYUpxlr2UbXYHWTuMacIdPB6IrmteAl6reYs9gkAbFmqh6elFJ2u5w0leiL91A88ir0PU/Femv6JpvUSuRep4HThvGLK7Hevj7tAEtazZkt58kGP/uG3YK+/3XYb6bHilsOGIOnb3GuSSuWdlCx4aErKB97KzVPzsJtqfcJGl9ZRNuHr6bq0PD87Wmx5bsCJIL7ln7J2N5J7pp8Eg3r1zDg0ONYeMUN5OTmM+LMs7CWTyHRoZQMvQ5jyDSs5dM6Kc5fF8KjRHpgDpuD9cmjGH1OgeqnUXuPJPlGZeq39n9uQhQPwtjrNJJvTOgUphA4cD7KYWPY8PCVaWMPReHHqFVkCgvZWiRrjitpUcvIGXZL6vktW/H5Owu+rp4/pH0zyvB3r8ccNq99nd6A/d+FPmFbogFh5mN9cA/ehqXIn52+BZkWxdjzFJJLJ6bFlitmPo4n0XK74LbWI1SdpsYG7jxvOJHiMoafdzXHd+8DaiurhUa3koI0pbxbSQFWo4VQTCY8kU66edkTH3LP7wYwe/ieTHruk5SiPnv4nnz++OeUjum3lXoiiyx87DQK+fZAUVH4m2/6AejSxWdGb4i3AJBbECI/32eZtTEJuA6u55ETMgnFVYIBnZDpX4+7HgGRpDnpEirIx8BFDQZQDb+LAqEigjl1/O/9t+l1xECIQaAwj8RXa1LldvzdUdiVy98WZW/r8bo5vk0dpCfZ98Q+vHzzu/z6T/txqNqCSpI3FpyF5ykIK4aIr09Z7kSwFGPfydhGLvpuRyDcBB4KerdD0y3kg6chkIBsT2lSuFH5dhJIoWDk9yb5xviNp/n7z0IJFGIeON+/x7P9d+xI/zPwUoqPGIXrBXGiVbQ+fT0nXTaLxmiUUF4hhfkqyeZa7r7waBrWr+GwMy7nV6dN4KL7/onnOkjPw5Yq0JQ6MVfbGlLKOPgu7X+beCLjH36T3C5l27B3OmNHz7fNsTXGq9fWljGuUVO8b6yv9HIoG30D1Q9cllI6ik6YjOo2YC2fljbWio+fjLTilJ4wDuf9eTjxDZgH3ZJxM6gKF7Qg5gGzkPE67I/uw1o5G33Ahb53xvJKwgfdjJPwUIL5Ga3Sqm4i40nWPzwJNVKUslR6Vpz8I89HEVZKGe8ot4PgENdCBLti/PIWEDYCifQ8JCbGsOsQehCZbMb++FFIRJFWI0KPYC2/GvOgmzAPusk/HEPgOQkM1cXRitGElfFdFTMn7aDCbW3Cs+PkHzKaDQ9dwYY/T6RoxATaPnwVJ1pF7eMzKBoxgQ0PXYEm3Iz9tLONVfju43XzOqxvSjCyj8nfJx6TCos5eNQ4npp7GWdcezvWvyvTlZJlf0IfXOkfRCpmSpYBECxBmAECv3kA6bnYn/wN78vnfBfieB2QxOh7CggNfcAFyJavfJ6BQCFKThmieBB6r+OQWzgQkok6NNPzWdqjVaiRIqRjgWtTYCZRw4WIrwm7kZ6HG4siHctnms4p+Nr7t9RmWwNSesjmL0gunZKywOYNnYko6IUQ396N+JvfbeP42Bl8CL7LeP1u7Z5ez+/Tvp7j4LbWgeugomSU4VKovrwyc0ELIRUVKeNIJ+Af2Bf0Reh+GJu0GlP8CSJYirQaU8/p+GutnI0+8FI0s5R1d11Mye+vQap6ShkfMe5aHq0cmzo4P2P+Yh6ceg6nz7g7FUO+8MozqX29ga5H5WYk3fQEWO/WsvCUgb7irwgCCYeio3piBFRyI9s+i8TOKDuz2DbYaRTy8vJyNmzYgOu6qKqK67rU1NRQXl6OlHKL174L6utb8bxtc8rZpUuE2lpfEa/90hcelnRpbGzD9VwkOUjbn/BG0MCIQTJuE9B8JaIl6WAIm7irUZ7jCz1X1XEsn2AjaJgoiuCjlW8y7OBBBAFpBHFbGqitrqdLWVGq/B2BTeu/q5W/edlbS4Buy/G6Ob5L+5molO6Vh8GXWJ9PwbarEXoZ5m6TkHo+9kePd3I5Nw+YhfXBPWh7T0CRFsl/p7ND2x8/hDHgIqRQ0ferRLiJNLdLY/A0pJGXIrEi0AXhJki+OS1N8VcK+m4kgNMC2O/MQt/vOsC39pVX9GDRlWekFulRMxcRKS6jYf0a+v3yKGrWfJRmIR81cxHzj+qHZeRy9xtr+MMeSsY4cyuR2K7jb2uO951pvOYFlYxKovQk8YaabyReU82KtDy4muqmxghs4tZ94Hykl4f1702siVZjxrKRbpplRh88DWG3IoIlPuvvp4+CZ1O/5EYKDv495gFz8GLrcT55MBVe0Zw0CXptqJEiCo+8gNrHZ+BEqwj1O4SCw88Cz85M7KaHsFbOgUABxt5nk1w3G9kx33pOx2pZgtfyuv9570lIN4DzycNoPYb7z/McpBNHaCZW9QqM0l+A9NCEhacEfBKld2alWZ2kkJSNvh7n/dtwPlrq13nIdFC7A+1hAKHc1Ht2fNYKynGkSuNm43JnHKvw3cbr5nVQVQVXKBQHSMmDI86cxGuPLODgUeNAOpmt3XqY5NIrfMLKDtf17sMRukXif1du7Ns9p2PruWjdhyM0i+Tq2cj17de6XoGz5hn0fucgFQMvVo3R5w8k/z3JJ8PKNH+sRuxVt1F89Hjq/nF72hjsID+MB31On82zFAAZyRK/KTxhW63JecEE7rIpaXM6uWwK6ndgB99e+4UdMV5/aN2+a/sahoLZ8iUb/jwRJ1pFzyv/3mkMKmXDgCas2ps2jvHdrsBuWYJReirKzy9CL+6f8u7YNIxN7z0S+/PFaD2Gp5Ur4xtQcipA0elyUiWNr/2ZomMv57wbHiWYX8zf5k5MOzi/f/xIzrvnRW6ffA65AYV1TRZmTi69Ds7BdmVG13RVQPiAcsY++l6ahbzh9XUE83rRZjvblMx1e+9rs8r/jsVOw0pQVFRE3759WbJkCQBLliyhb9++FBYWfu21nRHJZj9Psh70XeyTroWiRvBsnwjLCOsIKQglNDTFV7hjloOherQRpIvpT3BhbDwvkZ7/rHhrLQ0N/gRVgwFA4jXXbJd6ZbFrQ1MFeTkJQuEmBp9cjLV6CtKuBkDa1SS/mg3OBrSeRyIKfoax/3TMQ+aj730BUgmg9R2LSNSCm658KAV90XuPxFp1K8TWoxgRpJtEBPz5LQKFfvyl9DD2m4pT9z4karFWTOuk+Gt9TvF/EyxF2i3tLrht/vsXlFOz5tO0RfqRKWM44sxJAOSVdE0p45teV5wEk576iCffq6EuQcb8pIqqk8UPR8wKog+ekZ5XfsBEqh6YTM3dYwnF1xEMbJl4bdM8uAlCIN2MihHSQ4p0dnPn00cxBl2RVrYxeBrW+3el7hOBQoQTx3p33ibEW2fjoVN8xCjkJ/P979+7CX3gJejDbsJRiwlYjUipUDR8HE1L/0rJCZex28X30OWYi4i+fC9I2YnoSNvz9JRlX9/rlJQyDu3zbXUletGRKKF+GF0vBFVDCYbR9vwjzqeP+tbV1rXY783Hcz2MsqFIqwWvdQ3WqlsRiRpkuDvmgfMJHP6Qf0ihBnDW/D8UaWP0P4/Arx9B9DkNmYhi5ATY7eL7qDjvHtScQkpPm4vZvT9aQTmeFf/J5x3fFK6mUtuapCA3JyUPQnmFDDluNI9WjsVxOven3x/rfJlkt/ieQAU/w+h3GngWRtcLUUL9EHoh0o1i9DkGxdBIrr46vd/XzUXreSTWO7PAasb55EEQPlFfpjGs738dSqQHxn5TMbp0p+QPM1LKOGwkPwyJNl/xvntsWs75kMhMlhikbXs1dxqULR12/MRY0HcUvmv7Br2WlDIO4FrxznJ0wDkk11SmxrHQC5HSxux6PlIm0Hf7JfZHD6Yf0n++GGPAhaCFMPpf4K/7m8CfT2tIvjEesyhCwZHn48WbaH1oHNE/T+CkC/5Ej4FDU/c3rF9DrH4D3UMOIZmgTI1RX1ONmm/w1fNruOGEfmnErfNO6IeuK4zfzJV90nOfUHZgBf+66300uWsRWWaxbbFDLOQzZ87khRdeoK6ujjFjxpCfn88//vEPpk2bxuTJk7n99tvJzc1l9uzZqd983bWdDckW3+qtBVSkhISTRFHCeJY/qZWIBvUQalNx8n0h12LZGDmCNpFDvt6eY1zb2D2e5/8fCmt8/N6HdN0XlID/nWyuAvpur+plsQtCUwURszqlhAd6L0gtrh2QdjVCCSBDRRh7jyW5bu7G0/DIDDDCYLWCnotSNgyveqn/7D6n+Cfhe5yM/dlj/kl4sAzjF1ORTgyhGljv34W3YelGa7mWg773BYhgEaghpJX0GalD5RtP1/+7EBEsxWmq9a06p87ljklnpL1zw/o1hPJ8xd/z3IzWb891ufTQXjTHHf5b08Lo+U/ywPjjN0ml9hRqMJ+EbeG4HpqqEFD1XYbcamuiI64x/+Db8JwE9obVbFh8c4rlO9pBvLZ8I/Fa7pBriOsVxBOeH/toWmjCBs8GoaLs/ju04v4bU4OteQ6EiifTrfFe9L/Yny/GPHA+TmMVam4XpBPD27A09X4+EdpmRGorZ2PsPxd7kywCfthEJcYBN1B92+kpy2L5uXdTfMSolJu8CJZScsx0pBH2lf8VGz0+lHDXjRvjcE/MvFkIVCQuds3fcKPPgl6BUTaW5FebzLVuV4JZgDHgYuwNy9t5Gxw8pw3FiICiYfQ/F+uLf6DvceLGxhcCgiXovU9ESonw4shEC1pRP+w3r9zoIbDPlVQ/Nt0ncju5EhEpAjOXNtfcJca8qipsaLO56pmPGdIjjzNvepL/u2Ma4YIu5BaX+aErCpgHzUHajchEI87q59H3Oh3peZi//gtSaBgH3wxelMTn4zf2XfcrkULHWnPN18vZUCnGPpcjQmUY+07GUwOYB96IVIMIPYR5wGw/C4saRMTWktzUC2LwtJTregecaBWKZ1OVQfEuP/uOjGSJmytoqipS1nWpB/ASDRQEkls9xruDHXxzL4CfGgv6jsK3ad+OGHMVG1DoclIlDc/fSvLL93Gj1XjWR36ojJv05YqQqXGshPp1llk9Z6DvcyEkfo+0HTDyEWYQcBHBPKQLWq9jcGJrN+4DfnEV9gd3bPR6OmAujuWPMSdaRdMT13DSZbO4YfShgH9w3tpQQ7iwhHvO+XVq/T7z5qfo9queBEMad/5hAFL6hI0PLvuK4/cuz+jKrubofqaZbOazLLYidogEmzJlClOmTOn0fe/evXnssccy/ubrru1sSLRYGDmaL4ik9C3kSgTaLeRqUMNRJKEWjTbhM6rE4wm0PIWkFiai+rO8ydPocAz02i3k3Xp34z/LVnDYvnuiav7pnBddu30rmMUuh5xAPN0i7jYi9LK0RVYvPR2hFSAUk8RnU9NPw516FMPAoxFr/V8xBpyODXjVSxFGPvruJ2B/9ljKPU3vPZLk8qnp7mtW1FeaPnkIfcBokvW3pS3oGHsgAP2AudifPYLW9zSUSEAVgLQAACAASURBVDckOiXn3oMQgtOmLqCprpqn77iWNe8to7CiB1Y8BkBrfU1GlvUGS2Hs397jsD4FTDqwEDsR4cL7XgVFQSLQI8WsralPy8N+7+QxdCvMx7azK/Z3heNKRKAQp24d6+6+OO1a/tBjOrGYW8uvJrj/PAjkE5D1iLaGdA6DIdORThLwQOho/c7CIkDcUokMnoHz8f1oPYYjjHyEWYjlmURf/DNdjrsE4cbTNqdCj2QmHRT4oRShrihawI/Zli6eaqQ2q0bp7miGQHpmWo5pa0Ul5oE3Yn38UFr+XZmo862oXQYhRBxr/YPoRUci1Hz00tHopach8PAc259jdrVvQV17PWb/WUhPRSs/CJGM+s/uPZLk8nRmY2m3Yb05Ob2ttDCoAYRUsde9il62v++l0kHI9O71FB89nnV3XEDNY9MpPXchLZbBT4mB+utgKQoTFr9PUUjn2F46oYjJ8VfcwJNzL2PIcaM5fcZd6KKBxOpNwgv6T0dqhSgygZQuwq5GKoDXhtl9MtJtwa55lOSX12N0u3SLchZA6GUIIw8vFsV+5zooGIDR6xikGkDRQ+mHl0Ouwf7ssU4hG8XDx9G88kUKDz8dRVWRnoenBjIq3kgvY87nTRU0VRUpt3Y1UkTpyEtILpuTGlffNlPCt8GuwoK+o/BN7ZuRRX3ARIqPvZy6p+dR/+L9lJ1xPdJqwv7qZYyevwVhpsaxXnIKya/mIvRCjK4XItQI0qkH1cJafy3mblOQmiD5v0s3zp+uk4B8tIEXI9pO9jlj9DCU7I/RzjWDoqEEN0b7O9EqKir8MJvCih6cMn0hrz2ygGEnn5PmBXffJcdx2JwXuO6ZKNOO2pMFr65m1XrfC/WQPYqpyDMZ0iOP04fuhioEUkr0uENOYaCDkzaLLLYKdhqX9Z8Ski02ZsRASn9xiNlxFOX/s3fegXJU9R7/nKm7t/eWSgIEQpHQlOJDAYWndEWpoROBIAkmJEpJE1MIJFQhGDB0FWkqj/hARQE1NCUxEDAh7fayt+7utHPeH7N37t3cjc9CM97fP8ne3Z2dmTNz5vx+v28pRAtSaEJgmgZ9cZ+8Xg0zA1lXfaHHYWAWkacpfCVoSQ2GrIf/HzthN1rq6wmUidB90IxhL/Lh+NBDE37WotBreRR71EyEWZOpeF+Cu305qQ3nodzWIdXw/vfc+juxKk/FbXoA64CpxI59BBGvQMSrMMYcj/vmTdG/g2HCKvCwJl2Ddeh8zN1Pwdl8fTaMc/P1oLkovxVhmpgTTsVtXU5qwzk4m67Csrrpffkxuu+/DPOlFZx37S3sd/QpnH3jKkw7zpX3/walJOcuejiCoJbVjeGMRT+iS8XZr7YgVFM+/wiWnrQ7d154FI3btzH7h6vZ3tLOLT9aneVvevGi++nxvGGv0n8h+lXTB4deVJYzIQ762omLHkg2Du1gr5kDfjL0tX1rOSJwMHHIt1I4Ri3mXudFXtzOK9/CCNopP3kmna88iTKLsSbNiuCXyk/n9tAVGn7bOoR0cV6+mvQLk3F+PxvhdmBW1lBz3mLKjz0T5+Xpoef3II/pCELvdoCmwjK5pvC2/RLr0PlYe50Z3i+Vp+LW34nb+H1wG3A2Tif1ztm425dj1VyElheq/iqvCfyekEaRasF9de6Qe6o/MSPZNORcacpHSA/l9mDtdgLoNsbeF2edb7M0PAd+ohEh/7Ogwr5UlOeZzP6U5OfTj+G7/z2Op5bO4IzZtzB63O6U1NUMpRdsmQPOX0ltOAflbcFt+zFCs3G3Lye9cVo4L9ZchDDL0Ky6aCwHz7NAlJwoX+K9tRxj36mYo47GeWUGzm8vx3nlGszxp0XXlbvmhpzcW6Nmd8qP+Xroef7r83DXzEYL2hn1nWcZdfVPqL34TuzR+5I38SgwTKrPWYJRWos9el9qLlhO7cV3ogki7/k4A7D28mPPx1+7JBt+/Or15FupD+b8B4oeVYN++G1YRz+Mfvht9KgPJtkfjr99fnVdUGT14a3JVlH31y4Bt5uSo84j6GlHQ+Bt+1Uk3uqsmY89YlZYTNILw2S85iLc+jvD63/7coQw0QoOxNn2XQg6d6BpLEaYOpr0Q5TTuw/g/eHbWKO+EHmWOy9PR7htVHz1eiAsGummzbU/e4cz5t7Lbx+5nS9OuY7n7p6fdbwdDVsotRQNXQ5zn32XCz8zKnrvmbVN3HPGvnztwBFM/fE6Tl7xGpf/eB3dhsbnph2A0sHWdGx0bE0fft4Px78UwxifDyGcbpdYoRUVa/u8JJpWiPA7KYjHkFLRE/cpbjOJqbC8ZiSTQD7ECskXKRKeSXMv7J6hycsg7JDXjatDCEEqFRAr6EPklw0n5MPxoYShC/JjKTTho2kiq1Mjk+txW5/EGrcMTRMotxVrxBV4LY9GXR1hlmGNugZn0+wduOY3hZxX6eG8fE3oS3rw9QirJKzID+pCaqV7Y+59USTqImIViHhhbhinEEjDQBOK9I4J+5YbKDlqKV2/fTBSXD/nhnt4aN7lrP3109F2Tpt9K1fe/yKB7yN0nUdeeofPHDiabx1WhtaynrNvXEWyq4Pn71vME9edxY0rXmBjcyMXf/mzrP7jX6LtbG9J4PkBgR2DD1H0ZVeOFHlUn7uUxPMrKDzoBPSCUvTCSoIccMqguwOjoByMWO4OthELfZ/3OBOkh5Ap5F/uJbbXeXjvrMpOHtZcj3X4MvLHfwrvj99BVEwasOgTAvPT38P74w5WYuvuCj10fz97SIJrH7EMmWpB+clQzMvrwX/v0SyPaaWZQ0XbJiwAvRjwMMuPw9l2E1rBgVjV50YwZ8i+p5zN14f3XqwSmU4hMudjp539QeelvzMv9RjC7cR9bX5W57xfMFHEq1EZ7uSOndL/hLANjUXHVrHyoiMprKjhwsUPUlFeTssDV6EXllN00Xd3SucBEHpROJ6b5wwZQ3vcYiRGmKw0rYzmWXvsInC7EHYZzqsLsSfNCsfT7cZ9a/kOFIqbIgcAlWpGWCVZ+yLi1WhC4mToEf3jr9AwDIk0A2K1o6k9fzEChdvZSeKFH1Dx1RvQYwURX3iwuJsmvaiDrhcU51TZ/iA53n6gdhAYG07GP8gYfH4jKoLmIZRCJntyu1EUlhGvLmHU1LsBQupLsgkRKwuRQH++F/vgxQhNx6y9FGfroh2K6jdgj1tEOvEsmlGOljcRmVwfvY/XRfo3Vw04t6xfAcobMt8WHrkMq+JehGEhDZM826J4j72oveYmuro66WnLvjfL6saQcMP5rKHLoTQ/1IOpK7Y58+ARKAQzclii3Xf2p7C6XV66Zx19HWnyy2IcOWVf9Hz9QxV6G45dN/6znqQfUTg9HoU1A+I2fW4STS8Er5mCvBi+lPTGwwdIQYbzYqVTQD52QT6mSJAILFp6B25qpUI+eiyuM2LsWBKJHurKMwl5V/YEMxzD8a/GjpxxrfBI7LHzokWkMGswK89CuC2kty9EmGWY1ZOxR81GygB7/O0QJMDvyb041UtCnmqsDArGIDHQ8suxP3crGIURx7yfX27seTZIHxHLB6HlhHFKtx7NGoHyWnP+pmYMCLD4iUaECjjx6sUcfvqlPHf3fMYf+FnGTTqC2y84KuKXXXDLTymoLKCvsYn7vnt59Pcz563k57dfS2/rdp6//jzOX/YkB+4xmjfe2wqE/qbbOh32iOcxLPf2z0UQKLyikZQeewnND84MVcn3OYqqE+fhvjonShaNfWfQ9vwjVJ02M+pgD1GZVhJz74uiLnFEg3hnVdg9HsQTV6lmBBKrbne85jLMUcdmW/QdOh+x/1yseAy36X0Cn9BDd78rcuoaSKEhDAv3je9l//bbKxFWCdakWaDLUEl7B9SHNeIKRHwPhF6CVnAgZsUpKK8t9/Vtj0YrPBKr8lSc+juwas4LOeZHrUDE8rGPvguVbMXb8Cgq8U6m06+GnBf78Jtx1swZstC1DroW9/UbMfa7BretBaO0lupzbwLTpFCkEMGAMveuyiXXdY2WpIfqS1JYUcN5195CTJO0ZLzlK7/6LaS7Pef8pIKMWrJegEDLOYZIF4GD2/okZs3FoFw0eyRKaShfQ3ltobVdqi3c7s4KUGaolizi1Qi7OLon+pX0kUFYCD1gBiJehXQ6EX4fzprrQ8vJPSej5dehnHaMwhL0/GJUupfmx+cP4ZhXXboSpZsRrD3o7RrmeP+bxWD+/+B7eDAVob8IM+Ibd6JVHx4WyPt1OdrWoZsC95Xp2deZVYx50HXQ14D3zn14G36EsffZaGZF7nUBRvQsN2suxNk0AwjvH4zCqCjovrEo9DEXevY2Us2oVBtWSR7KLMHp7qb9gekDBaSzFzHlrl9wz+Vfjp7lX1n0OIveDBGqdcU2NYU2T196MNsSKW5/cTM3njghJ4/cl4o1mWQcoK8jzUv3rOPz0w8g+LAGajh26RieIT/gkL7ES/pYeQPL8LBDXornORQW5yMDSV8MpFAUpsOFS54fKrPHYwamSJIITBqy3A4EShpoustue+1FZ2IrNelOtPxRBNv/9BEe4XD8J8SOnHHZ8xIuENttSfg61YkwCnDevyaCn0W8sJpLUH5zCKUdcUVuDqRZhpQC89DrAIWSPdm2P/vOwyPk7BrjvxJyWvNiQADKwx5/C279XZHlkzXmBpAZ1V+jEK3wSGTPS1m/iaYzdu4zeM0NJH77KL7nQm8HeZbBmfNXEs8v5tbzwq7XqdcsI6+4jN6OZoora7lv2qlZvLNH51zE6dfdRVFFLades4z/vXses865jq9/bysjq0q5d9b5PP52N+NrSj+C0dq1wtAF0uugpMBBoNH6wr1REpD8y4u0AFWnLcuIthkEPpSfNANPy0PXi4eIo1mTZoEKcP+0NGcnMVcHUaVbEPEarAO/jfPKzCGcdfvwpbQ9txK3eRM1Z83FOmoFiF7cwboGI2bibX0Oa5/dcNbMHfrb+0+DeDXIbgRB1j2i5U3ErDoT7ImAn7mvJuO2PIVRuM9OClKNWDXn4LU9E92vdu0URMxCulvwmh9AeR3Yn5qJ986TmBPOA6sY96WrsvZNOR25kzy7DPOI2/D7etHyHGovvI3Eiw9SdOCXaPnJvH/IEuvfNVxNY/oT6/jOEaWc9s359D6zkLyvzY2uT7tmN9zGxdijZoZJdYbzL8wy3O4/Y094GISBMK2cY4hRgnLrsesuBaHj1H9/wNZuxCyUysc8ZC7e2tsAdl6Ayqi4W5Nm4r77KOaka9DilYBAJZuQehxz70vwNj2OMfY4tKIxyN4GROUkzJHH7lC4mkXZFy/Ga2scwjHXC8vRCUAIqs9eTPPDs+hZ+1vKj56D+9q8rCKW70kK9NTHWrDpTzy9RC8Fur7LFo92lmDnes/V87F6t+e0teunIuiF5ZSfcDV6XhF+XxJzr8m4a+ZkjW8/0kiU7oU54UywdISpo/qa8N9/EuuQ2eHOCYX0OnLPYSJGbNwSVNCLMMvQS7+E7H0De+wCvIY3sA6ehfJ7Ual2pO8gAidK0iHb4s/cfxqxWHEkYOgnGml5eDa1l97NtId+j+ukEYbNjS+1s7YxEVmatbzcQMEh1eTbOjeeOAFT13JaohmaiJLx/hgWehuOfyWGE/IPOPoV1s28gcpdl5NG0+J4jkN+rCwEV2nQk+dTkw7v3nw/rKnl6z55softIs6WTsFgKJaUBprmMmbPCfRs2ojyuhH55SinjyD18XmAD8euFztyxiFMylXZ6Xjvr8bc8yQIulBeUwiVzSimhv9fHAoVeU0RBzJbUXUuUpl4jbeFvNjWJ7FqzsUacz1IByE0VJDAPOAqhOeB0FAiBV4T6W3ZPsyq5iKEpiMQyKAbZ9vCMOkYOw83s8/9HunOlvnheyO/Q+XJV9L50tP0/vZBjNJais9ahIPirAX3IzSNh689b0BFffkTkVd5f3Q0bKFi1HgeunYyPW1NnDlvJSV1lTz+3cupLivCMgxOmmhhDLui/EPRj8xIv3ddNM4VX5pBc097pLQe9LTjdXXRPHgBefocRGEFgVGAMLVIHA3po4x4yInOlWRaJUM7iAfORmkW7svTs/yiB38PJGXHX4Lqa8Z79duYn7ocp+HOIfZU1tgrUE4i928XjgY6cbZen1W46tdd8JJbsGJplNuBCjrx2ldj1ZyL17Vm6D01aiZu00qU1xHSQQCz4iTS788c8hmn/ibs/ZaAMnOfl535sSPRlEdy7Qskfnk3I658gPJjzkU6PVSd+i3an/8hztZ1Ude0l/gHcEV8ssKXIdf0njd7WfHfE2hMNBIkuzFKayk89GQwYyivAy/xAlbNOTib50bn39ptEUIIBD5gYI25IVJTj4qKfiduBsobjVnQgUyux6lfjD12CcpJRtQBrBwFqEPng1mEffhS3HXfBzeBQESFpRAFsRR30+OYe50aumE09fufz8Vb99AQFwH7iGVIN5Ul7maP3pfy46fSuOLSEL0y8ShqL70Hmeyi5ef3UvLpKRhFZej55SR+9zhdmbn24yrY5Or27orFIyXlTo8ThnrK99OCciEfNOmhF5Zn+daPnLoCd81NQ4qU5n5X4LkdQxxWrLELMfc+PXsuGv1trLE3gt8aOrPINFgjEUEiopuFz/j5qJqLUX4Ss3Yi6c3XZL/n6BgTv4H7cgbKfsgcvLW3R3Qc97V5lB87hYb7ZkTHRuDT19xAwgG7ahzfPno8lx/oEvR5bH58Ix2buzmo2CJvfDHfeGwtt5++D0tPnRjB1iNLNA3yy2JZSfmw0Ntw/CsxnJB/wOH0ZHuQA3Q4LkopXNclHrOjv3fl+cRUJhGX4b+WcilRPfTp5Wxpz17NS6mjaS6FxcV0CQtTD9AKQpK531EP1ogP9diG4z8jDF2gablh4SoIMPc6E+f9mVESIfQBTnf//1XQE1a8k+txm1Zm1FRLwChCKfDbnoh4sSH3dQ7WyGkIoyTs8PW+ESbV7z2NtfdklNuMs315tnK7dBCawG1YGXWi7NHX4nb8CmfzHOxxi8D/OhhFuNsWR3w0Z/v3iO22lLLPfQ2Z6qLn1WdofWQ23pGX4imdn2Sg6RAm3s99fz5nL7ifno6WiD/e09ZEonErW976IwCPzrmIC+9+nmm3PcbcC09m95FVVBWYWFIOw9f+gRii5u814bUspeachXjtHQTJbrT8kigZh8wC8ifzqDhlFlrFWJQS+GvvzNIh6Bdm2zHJFHZx2EHcfxpafg2ydxtKSbwMf7q/0zgEftuzBYRJsPlnmPtdgVY0BtWUA4JpF6FSuRNcYQic+lXhvWGUY49bhvKb0YxSpN+FWbgv6Y3f2iGhfhCz7jKU10hs3GJAR2k2XtP90fWtxcZhxsbhZL7bvy+DeeZKemh6yIm3Dv8e3tsPoBLvhOdzy3M5UQYy1YrQTOIjd8fZ5yjMuIb72gCXvvrUGTQ/eTvO1nW7rCe0oQnqim3WNvbiiRqM0lo6X1xF9dkLscoqUGjYo2ahlBMl41reRMyaixHKwa3/QTRXaWYp1rhb0XBRBKggPVSscvCYZXi0QthYn1uJpmkorw9hFmMe+B20eDmg4XZ1gLsFPb8Ya/+pADgvTd8BBZHAGHtclDhFv7dlLtbYK3CbsikcqABh51N97k0RfaT0mIsjZARAcv2LFH36VNqeWoyfaCT5lxfDc1ZaS/kJV9NFdrL3URdsBgvP8THvy4cZQV9ip8cJDHmv+cEZlJ9wNcn1L0bb6Le1k5pJ6TEXZ/nWG0XlODspbpoTzhxyTeGHz+4sVXVhI5DRM10rPDLjSR5EWjQyuR5n8w3Edl+O0jScTXOHcM5j45ahpEHsC49BkIYMhL0fJaJSzRgFxdF+GqW1eO3bKKsazb3n/hfHLnic2IhxrL/vnazEunjvMt7rSLHghAkIIVi9vok7vrYvuhAESvH0nxs5+6ARHDll3yEccl/sOsWd4fhoYzgh/4Aj8iC3B05tpytRfpiU51kDtgxdBR6aHn4+T4YlNRlITAK69GKaesHxoX9TSupoRpjw2/kloYiVHSb4bttWqBtOyIfjXwvb0sg3G3DqVw3pwlljF4b87YynaH/3W8l0lLz3J+KDO+MyuR63/k7ssfMAC4QRwm4zyXv0rxbD2TyX2LglOM7mMKmeeDsIGfIod1BuRxiR+nR2B34+ntcIfg9osaxkHDKLBBTu2jspP+ZcAHpefYbCkgp60i4dDVsYs/+nOemyaymtGoFdUMJji7/F2l89RVndGM767v1Ydj4/XTxgydXRsAXf81g69evc/4vfce3kE5FSDYu7/IMhciAzlNeEdHtouGdKxFvO6aNsxaG7mbaXHqHyhLl4r4UJpUp3IBVYh84fELrKdFPcdx9FbnsOd9tz2EfeFsLRj1gWJS/+e4+Gn3t1EPw2w/829vsm5vjTQvj5py7PXcByuvG3rB7625NmIv0UZvVZEHSj0BCyC3froowY4rdxNs0cmpyNugYh+3C2LR10vS/AKD8Z5WxGeR1IZxuaVZebo2mUoxUeidAk6fdnDGzjU7Nw/3wvpBOYu5+OMvIx958WcpT9NEq38d99GGPM8ehFZVSedBXumlnZSsvrllJ+7BRanrx5l+ULW1Ky7LSJTH9iPW19DpVfnUfi8TlotoUUBhoObuJ/sarPHkjG66Yi9ELchntyzFVzcepvy6B65kf2df3RPz9C5npKd+K/vxpzwjk4fxwMCZ9Hx4s/IW/PIzDzdfx3l+GlmvHj1diHLcmNgsgryX2NxIZSONAMhGHR9cqPqThlFmblboAaAmHXrHhO6zQ9ryjr9cdRsBksPPdx78uHGcp3d36cauiY+YlG9IJsalW/WGOKPAoqRmehIoRu5S4wWiVgqCHXlNBiocbMiG8hNAOBhhBGJEyp5U3Eqjw1R/ExFDVESdBMYuOXgfKQXgte033I5HqUmwDHzba5nDQrnK/W3RHup1TRMVV+9Xo6Vt9F5dcX0NGwhQJLR7cEX5g+HgKPQGpsf9sl4QZ875d/paHL4fN7lHHJEWOY+uN1UYd88fF7EpcSP1/n89MPCDviGvjiH3/m67qGocS/tI3h2DViWKP/A450d5gwG4Mg692uQrph9a2itpaDjjiMAw/7NJV7jkLmhZ+3dA2lQLlhgt6qVyCVYHvXwLal1NGEA0Ijr6gKgER76EHutW370I9tOHbtMHRBvt2Ns/n6kIOa6Wzbu99LbPwyhB4DrxHpNmR1vxEW9th5gxLxWSivI/z+yGnE93oIe/wtKOmggi7wtoWibpnkffC/ymtC+V2YVWcizDIIOnC2346CyPrHrDoTr+tVhFEUddl3rJybVV8HvRiVTqG8jqzjFGZN6CU6/jS8DQ9S8d+XMmrafRRWV4GU7Hf0KZx37S2YL62gc+WltD9wFadOmcmY/T9NR8MWHrnuAjwnFXXHIVRqLYhZvPjGei464b9o6+7F0rMFZ4bj/w+ljGic+0OYNfhd4RiGHZ2ZlB5zcdZnjNJapJsiSHaT/MuLtP58BeZht2AcuhzGTKH1qVvxZBzzM0uxP/9DzP2n4a29HbntufA34tUQq8T69EKEXYL16YURN9F7/xnsw5cQO2ZVCMt8eyUy8TaaEYu4tt6GR7FH7GhPNRN/82rMCefibXsB+7BF2EfeFm0DzUJIB3f7cvBbcTbPDReudVMRDF3YKq8JzazC2XzDEPE3ggRm7aXYY+fhNT8Q3aM7nkf0fOy6S4d2YusXYx/y7XDf1q/A3/xztIKRYcdJefjvPoy513n4bevQ8koRmsittFxUFsJjyWNXjCCQlMU9lpxUR3lRPmZ+ERWnzEIvKEXoCiUszNIvIJ1tofBlzcWAhkDtZK6ai1l15sC8VT056/cGz4/2mLl4Gx4NRQgzBSIYEN0rmHgYdmXVEMsx5bQPserztzyHsMpyXiPCLos+30/hcN+6HStPR6a60Aor6RXFBMIaYkvYD2sfHEZpLUGyO+v1x1GwyWWjuCu6BAhj6Lj0H+fOzoFeWBH9PYK4Z3jnUo9FlnfVp16Ju+5OrEkzs6+RQ+bie35IAdrhmlIyjVk3DYGPs2kWqQ3nZglT9vuS71h8DNcANSgUONtJb5xOasNk3G1LsWqnoBUeCXreUJvLNxcj/CQq3YF16DyMshGM+s6zlJ9wNR2r7yLoaQ/v47oxYFqUuC203nsxjTefQscPpzBmjzR/2ppg5jHjWXn2/py0Xw2r1zdz19f25edTDmHZZ8fR8cutKMLE2ZEBDgFOBuX6j9ig6bqG6gv49bI/8fM5f+DXy/6E6gv+ru8N263tejE8ih9wON0emi7QzYFT2+OD9EJBiOoRdbiOg5SS3fbcg7x9QziNaeo4UqB7YULeYYQJ95auAdi6CgyEcBGaTlF5OOm1bX8brDy8ju0fyfENx64ZSkkK83yEkANCbsn1eC2PAh7Sa0UgEVoMr/mByBtXJtfjbluCEjb2uJuwai9DobBGzcCqvTTcjtLBa8mIrkm81icQRjFe+2rsUTMH/m15NLMA7UTohZjVk3E2z8EsPy7sII0a8DE1K45HOtvDxD6nBZqB++oivHV3ZzpPgxKlMfNQTiryPFfpNvw10xBvXkdNXRFnXXcbvc8szIL19T6zkJMuuxYIu+FFVXVZfuXn3/wTXvnJ3Uz+/P7kx2zKCvKxdWP4wfkPRl86jjXmu1njZVbNoGP1D6PP+IlGzIox2QvI0+eg5ZfQ+eIqIBR/89qb2bb8Qhrum0HyLy8StGym/q5v0PjgDShpoNJhki/i1RiT5qD6WvDeWk76VxfgvbUcc+9LQj/ntjeRQYCS4K29MxIQUl7fwEIw8Q7un1dilV9BfMJD2GOXgFGHNfESvA0PIjf9GOV04rz0Tdw1N4QJvVA4/ZoIGZSIWXUmBN07T6iFyH29azE0sxIV9IX3bfMDUZGs/7vWbgsRwgZyb0O5nWEXP92BOeoYQBnF4AAAIABJREFU3HcfDjupVgnWflPxtr2AtduJtD69HK+jMacfu1ZYvctxcgeHrgvQBLpegqECWlZNp+n+aSBE6CqhPJxti6M5UjOrIOgMx3Nnc1WmA668JjRrRPZcNXY+2HuEugB6EaJgLFrB6JzFEKu8Fvzuoe8pFVE2IBwnc/xpuFt+hT1m7hCPc/fth7OLR+tXIJtfwX19HpWnXRONb4o8qs5dmnUf6mUjqD735qy/VZ+zhJ7Xfx69/rgKNrn2d1csHun5pTs9zp2eA6OUqktXMvKaZ6i6dGXWPZxU4Xcqjr8Uf91SZPMreG+vxNzvCuwjb8M+bBHehgcxLCu05NuhMIlRjWYWZdv8ZaxRgSzKW3/0O7HYY+eivPZonux/z9m6EHvEZYCNdeBsrEPno5XuHb6fakbEq0LRTKsUlWpBE0k6X1xF0NNOyVfm0Nywja9+71HGV5cNgfC3PjSTk3a3+dnaRlKJFmrp4Iy9YuRbGtt+8T5NLzew19f2oFfTCUwjeqb/M8m1oUQEeYcBpXZD7Vx85p9N4ofjkx+7VmnwExBOj4tdaAIDgmypwAA/vOF621pp3LwFpaCisoI9JgR4nk7MNkhLDeGGiXt3rBJdbOPtVjh6XLhtKXWECBCaQhgF4fZaNiIqR+F3NGLtuDPDMRx/I/JiGjE7hcAncD2EbEf67VnQ26jDowJAQ8n0QPe7nw8m0wg9H5QCApB9EQcMIL7Xw2BUoLwWvOZHsGrOQxLDrvsGShiYFSdEglSROnHFCRHsNlywStymeyO+LYDb/AD2qNm51YrREYVjMcd+EaHnExu/DIUBykcoH6wAYqWh97nbCWQ6fOuXYh2+LCesr7CkAggTcM20ueyeX9KbaKW3o4X/XXEjn5s8nXzbxC4sxEAHRPjgHPYp/bvDDxQ9Tg3Fe9yFCtIo16X1qdsjQTfIdHt0m+opoeiQEBpeVxO9f3yCsuPOxygoJkinUH42FLUfTusnGml+8nbKj52CXbMbXls9QV836t1lQzot9hHLUGh4IoaW7s3iVis3m1+uEu/g/fmuyAPaPmJZ2F0aczxi99MhnunAGzGU1wNKDlqg9kTFJgC3cUUOMcT5KL83NzRephFCR2ixUJG4fTVKOgPaDWYVBF2kN357p84HGIXh8Xo9YBYgtz2H1/ZmCNnf8ABy23Oo3U6i/MSZIAT6DjB885AF9Lj5u3Qy3pBspLywlmJbYAyGBmsSZ+MNxMaFiYPymnCbVmKPvhahxTLjmXuu6rdD6+8GWqNnoxllKCQq8NA0gdJLEJqOscdZyL763LoGfQ2gvKHvaSbeX76PfdgilNsTqlBnUB5e1wZiBywPYcuFFbivL0Ql3kGNOg7n5elZxx9yyWU0vkGgSMVHhOJfykcKgyR5YEDVpSsxRICvdBw9n5ITZ1F28ix0QwESnRR9bhz/I7xWBu9v/77tiirrQtOGjMvg48z5nitxs3j0A+ckCBRuwUgKS4pRxbNRXg/+e4/irrkBIJznml+B/aZCOhEWJidcEdo/GgUorQhUX9Z1nyX22o+Q29GJxapEep0IZM6EHcB9+eohVCKV7kD2bgnn4CNvQ6gA5aepueAWvGQnhmURrzHo8mJoQW9uCL/0mbqXz0PfOjESd73w1qfZ66Q9aU35XPDY2gi+vuy0iZTGTaSCt3/x13/MBk3yDyu1G0pEa4q/+3eG498ihksqH3A4PR5WoYVSAxNaftFYTBV2vt20Q/9bba1t9CSa8HyI59mkpIGe6aR360VUlti83jBQKZMyrJ/oRkDQn357CYiV4HdmTyrDMRx/K/JiGrbZC34Hbs/baMLF2Xx9VvcbiDo8zrbFKHTQSyJIurP5epytixBGOcrrBMJrHGFi1k1Fy5sYdtHTm0hvmoHQCzCrvoZbvwx30zdRXitex/+iWSOwai/DGnFFpLiOsJBeywCUPVMIcDZfj7NtIQgjLAy0PIY9du6QrpLb+hTWxMm4icdJvXM26Y3Twd2OUmmk34rb/AOsT10CsUpE0Thix6zCOvxmRKwMgU/xUedmnS+jtJaezrawG77sSQzD5PtTvsitk49k5bTTWPvrp3n42vMAhQg0gkD9zeq3rmsEpoGjZ1fZhyNMyjWzDL9X0fTgXIoP/3p2x+3cm0iqPHr8OF2yiB4K0fJLKP38CeDejdc8HZW8DbPcxB69b7TdwXBaZ+s6Gu6bgZtIUL/im2immbPr6He34HYmwPNoXDGFxkcWIvacTuzoVWDkYR04Oxu6OWkm/nuPhklRBqrsrb0Tb/0KSLfhvbUc5+XpeGvvRKFH1+1gPYYdi16x8cuxRk5Dafm4DbdGSBHIXO+jvwN6CQqd9F8vx62/M7yHrJFosfEIMywk9UPd+39rR3i9+/qSaN+EEAMd0gy0X8SrqX9vPS3NTfTKfLplLfrht2Ed/TD64bfRo2o+0gTro440KZ58/QUSScX2hkaa3t8QQXkFXqh14LVG51Um1yPdxoHxzDVXjZoVIYPsUTNxG1YglEIGvbgNPwC/CZRPeuOVpP96FUJP4Xe+OxQyfOh8/HcfCDUPdnhP2KWodAfum0tAuhHKQ8SrMcedhnQl25Zdgrt9I6QTAJGY4eDI5SUeBIreIE63LKQ3iBMEKvqbWVpLbxDHdSVp8tBVN94r03BfOJvglW9SKJow9I/WhmLHfdvVkvH+yDUuf897ucLQBfmyEefl6dH8YO59EVrp3lkWewoZWkymE7h/mIP7hwWoQEPIFpSSWYgfmVyP2/pkyAs3KofeF2PmIN0WCDojjZrBIcwaVLJ1iJWksedkrANnD8zBbifum4tBegi3Hd64Fu/Xkwl+Px0ttQ1X6bnh/cBD38q2Or3vqpNxetuZ9sT6yAKtocth+hPr+UtTL+c/8mfKvjiasrEDegn/rw1aRql9cERK7TuLfyKJH45/jxheBX7Ake52sQvMqMCYFGAYZeAlsUwDoWWfcun24KYDCooLkKIczXNRQKHlYRdX8Nd2QVfm3lMy5KRqRoBCx/M1iopMel0fvyu04hmO4fh7wrYdNOHj9azDjI9Aua3hgnKQKnps/HIQIrQl8Zog4wighE5s3E3EJzyAPf7msPjkN4W8V2Hjtf0cIR3M2m9gj52L3/X7iCOJckMxFq8JZ9tirNLPIZMJMCrQ7HFYdVegRAx32xK8pvvCBWv7atCLokREJtfjdv4ee+x8ZO8buPW3DeKqL8fr+iNB+2MorxWr8lS0vInR7+E1g3JDu7X2n4LfjvP8OTivXINAYOx7JTKdoOy/vkrePkcB/bC+myjbfRInLXmGK+//Lelkb5YNGoQPbel7UaKd0gV2UVg4KxtbxIGX7cfECyfimhotruSCR97iy/e8xgWPvEWLK4eT8h1CaiZBTzsdq++i/ISrqZtyDxWnzIKCyiGLSLMoD695SbY6e+NCyr8c0iaM0lqSdgV5X7kxK7knryzkuPZ25UxAgu4OWh6cgRaE3VBn6zrqv3856eZm/K4eAmljfXoxsaNXhbz0TIfGOnA2WqwM99V5ECvFPGgmGCIUfyvdK+Sdb/pZRKfoX6AKezfIJGrZRa8y/JaHUV4HyigjNn458b0eDu+/QfscXeub5yBUGpneSHrjdIRysqgo/fd4fK+HiY1fjrf9VVTinTC5O/g6pJcC6Wclb2q/2fz05utYedUp4HThB4quVIxEuoCuVGyXTsYBPOkz+ciz+NaT68nXA37/7I+ovuB2as+/EQhdKbym+7BHf3ug0NL6VDSeQ+aq3RZm3vsOsXFLwRoZooZQIZKo8tRw7ssoR/frBZhjjh6ADB+xLEQ1SC/sDCbezoITW0csJ6lKsA6dj0p3hO/tP43Y0auwDpiBt/EJgs7wumh//ocY+85AxKszif2sIUl/n/vPqZHnWym8V6/PSqC8V68n30r9i6MyHB925Bq7KPmdNDN0ZZg0E2/DQyjdxtx/GvYRy0LnCtvE2XxDWATckUJTcy5u84O4W+aiRBx7/M2ZOW15pvhfCGZt1ATILrovwF27Ims/VaoZLb8Ob/2KDH98Pn7jK+HfYyURmqf/s3l/+R5JL0XlOdkQ/opzbqKlK5nz+a4rP8uPHMKkvChu0NDlMOu5d/nUhRP57KX7UT626P9Nrn2hOHLKvlFS/ncptf8zSfxw/FvEMGT9AwylFE6PR8W48LTe8foqRuzxWTqTOtLppTAvPgSqaukeTsolPy+GWzwaPfgLyrTYLdbO9vzRKOp5rV5wzHiFDDIdct0DbKSIU1oSp6W1g93wUb0diIKKj/qwh+PfMDRclNeBVXIY6Y3Ts2Csod3I9Qizhtjutw5SUW8ErQBkEuXL0DtULwEkXvvqEGZuj854i/8Ue+RU3JanMEuPQTmbkcn1aGZltA/Kawo7N7+bHnnjopvgutjjlyGUh8LEqpsCGCidsKKOQgXJiLcuhAboSAz85vsIEs9GXHS3/s4s2yChhQ8yZ+uikJeZQa70Q5TN/achNAPlJKg8dQbBl6bT6ytuePxlfvfWX1k69eshG0VolNWNyXpol9WNQWg6FzzyVgRnW3L6BIpeaqDgiFqmP/cuDV0Od5y+T6TgCgNV9vvP2h99GMoeRT/fseXBGTQ/ODPiOyYDG1DouiBPd6C3lSBwc8Ia7eqx1M54ig317Vz5/dVUlRZy84X3YOmKt95v4o4fv8LVp8yn848PUXHsDPx1SyMIpLFvaOPlJxpBySwPZqEJ6u8ZUNm3R+9LxZcvxzrwWtA03LfuwNzzLIiVhr68W2YPUjSfifvnlRgjjkIJE3vMDQijGNCRwg75yHYBsd2Xh7B2oaOkwKo+C6rORJKHlN3gNYfXs0yDWYjyEqFAUv+1rjzI8DP7OelZSXnm3ggdEObC2FMQKkD6KXyRh55XkEn2AtqbGnh45hQ2/zkUMpSB9x+3eiiw4qQCk4YuB1+3Of7MS2i+/0pGXnk7ijzsUbNwti3GbbwHa+Q0NGsECoUS+dnjiYbb+hRG4T5ZY6CVfhmr7GiEMDHLj8ughSYj5cBKO5zDQCbexl1zQ5goHzADf9OTWAfPwX1tXpiUr70T69D5pGQBqbTEsMrQ97sOo7AY2VuP++aiKGnx3FDoy9m6jrbnH6HypMWodA9e2sc6fBlID691G54o+6eLLpry8XMgUHY1hfNdMXY6doWjUBgYY0+MKBB+7xaMPScj4pUhzU2FrhlB4lkA7HFLEEIHYSCVjll+ElR9DRW4CM0I5xV/G1psN6SfBL0AtBhQFt4/0kP67WCURGiO/giLRxJzn8tCO8v3f4FRezhe25ugVE4EVFWBSXfapOriWzFMA9/zSfT4JHpTuZ/vhkVdsZ2VlNcV23Snwuu4ocuht9tl/ePv8ZnJe2MXmX8zuQ4Cif4PKrX3J/HDdmu7XnwiaypHH300xx9/PCeffDInn3wyv/vd7wD405/+xEknncRxxx3HhRdeSHt7+8e8p9nhpwOkJzHzTNK+w+9b32GPuv3pTrlIz6MgL44vd0jINRenz8G2FZ5ZjNAqkKbFHnYTMr+Ckjj8fEMI65KBCYBuhpNBIGKUVxSybevW8P2uho/waIfj3zpUgAo6IcPPygljHTsft+v1qMPjtT4B+AijAmFWolkjQSYHujnNDwAKr+tVzPLjQLoYhftkK6YKA3vsAmLjl2PvtgiFhfWZeViHzgZd4b1zH8LvRflppLMF/GaUsxl32wKcDWeG6qwAQQ9e4z2R1RME+K2PR8l4v0jcENsgmY7U3IVeAoE3cEpSzQgjBrqNcjtJdrXR60oSvsVFJxxFVUkhM+74EZefdjTbO/o467v3Zwm7nfXd+2lMiqxE+5r/2cDo48cwK5OMA8QtPWeV3ZfDD9TBMZjzuaPYkK4L4ql6gsa3Q1/kro6csMZAWSiznDytgpsuPJ2rj/siv19Vz9YGuOre/+HR519lyn2/ZeveZ9EelGEdvixSZu/31DZKa5GamSWGtKOatLN1Hc2PLcBNgusamBMmo9wezL0nD/V6rr8Jc5+L0eJF+JkOqFP/fWTQh/BbcDd9E3fLDSinkfTGq0m//XXcTd8EpUhvmglBKwgTYdUijHKEVQtK4jUsz7rWpZ+I+Mm5RN4G3yPOlrkgPFS6DVJNJHr7aOjR6OwJ+O5Xj+DG0w6NkvGyujFouvmhjv0nLaSU9Lp96Bkf8uKYQfdTN4YFGqGHiYdmY42ejT1qNgDOtoU4m65BIx0WJN0m0huvBq+JoP2xSMyqf+6ViV/gNj8MRhFabBz2yKkorQy/5YfRfoTCfgNey9akmXibnkQf8xXaf/VjGDMF4+CbEeOvxE30ont9ACQDG6lMOl78KVhVmAfMxjp8Ge+5dQRFI6iZspJRM5+g6NBTaXr4Bhp+OBuESceLPyLdsAUVK88UwnLH/ydeKYXxd0Hgh+OTFzsbO19ZdDt5YJdHApkq3QGxciQm7rp7QAzQcoLEsyjpg9BxW54E5YAeR6AjdAsZpPHql4RFRhXg1d+ERhr8BMpvw9l+B9KtDxN96WMdMncIgkNKP4Spv7EIuenHoSDlpFkooec+hu4OCo3eEMr+m/PhjWsp1LsYU1fD+cufynq+f23JT+nSClh66kTqisN7oa7YZumpE3nxr23R66DPo68jzR8eeBvd+v/1YnZUah/8ed/3cfQkfaIbR09iWhpBIBGZJP6EeZ/h89MPQAzr0uwS8YmdDW+77Tb23HPP6LWUkpkzZ7Jw4UIOPvhg7rrrLpYuXcrChQs/xr3MDidjeWbm6WzvaeLo/U+mM5lEoaF8n4J4IXKHRbclUrhpn7w82NriY+Hhx4qZaG3HEpKKunJe29jO1i7J6JJwEWQY4WLeVzGKi0y2bN4EoyeiEvUwYv+P9qCH498zhBGqm4+cmmVh1i8CJaxK3Pb/RSZ+gShchhI6dt2UEEqGjxIWytmI0GJhNycjyobQMSuOB78n5IBHXuMl2GPnoZTCrb9zkFDVPNyO1ciml8LXE+ajRCGaBuktdw4VejGKcZsfwig9FrtuCiBB2Ei/G6vqFFTJEWFnPONhmmUbNGoWSrPxGu7IbKsEmWob2H68GuWn0Qrq8N57jJ78L9Cjwfm3Ps2Ka85jwSWn8eWZyykpyGP+qp+z7ILPcfp1d2HF83FTfRRW1DHvxbas09zQ5aAMLSsB7075OavshibYmSqLrmu4moYvFYYmkOo/I3kPAkVvDrGhOElaHpxB1dfm4ica6Vj9Q6q/PgOvZcCf2xizAJ8ilAx4/e4N2Rt+roGVsy/gokX388a7W7nq3v/h7qvOJR4rxJbdtDx5M36iMcNZv5mkykMvyKfi0hWowEVqBtXn3hQWA/o/d84Sel/9Cfm7fwpVMQJRNA6hOaiGHPZl+VUorwOz4nicTbPD+04zcDZdi/KaQlTHDsrCyu8MX/utoFyUTIIWQwU9mcS6Y5BF1nWhGF3Lo5nvd6CCvrBza49CpjcN+Pxmto/XifNyiFYpP3gunRhAMRfd+hQrrzolEje66NanwC6GXRyiPjjaejtp6e5gVHk5S07Zi0qzl+YMWsJtb8aqqsVrfgSz/DikiKHZY7BqL0UpH0UIXbdHXBaO4w52j0PmXmHh1N+JNeJKEALZ+wYwANVVGMSOeQiEhq8EauJUgnSK1F9fpefVZwb8lp+9lcozvgeE91FQOhYOPZM+38PxDdb35eEEkhGF0O3HicXyiVUZVH1tHsiArtd+RuEBx0FBJcnA3inXOFJ93qFjp0oGPt/nxik8ZEEEfR4QAYwD/znX0b9j9LlxCg9dgLdmYOzYdw6pXhtlCXpUDfmH3xaJxPW4cTRfULDXeUinD3vsXJzNc9EKDkTTbVABRuE+CGGinG1D5zCZRiAyhXY9LPo3/SBE2NVdgtNwL/aIK5GxCuwjbgmpdMJASh/3VwO2gSJejYhX4LbU0/vq45QeNh/31RuyEFCB6+C/vigLym68cwvGoTfj1O7JGXf9BgKP/Lw4Wn4ZjoR7f7uJmceMpyhu0J3yufflLZy0Xw1rtnSx+Pg92fz4xvC8daRROe4Z09LoDXrxpY+hGRToBXju0GTajmls72ymtbuDtt4EP17zHNOPO4/RRSPxXDmwVBjOw3eZ+MQm5DvGunXrsG2bgw8+GIAzzjiDY4455hOVkKd7wm6bEdOp72vj4D0+R1NTM0opkskkhXl1Q75jkiLlSTRNsLbxHT7d10Gyci9svY99YtvZULIvhvYbnlyvcdVhodK6boSkcl/FMQwQpkIBQWf9v8+ADsfHGoG0sWrORUoNe+w8nM1zIhirPXYeUhmYRYcgyr+IVBpCi5HeNDNKdqzd70IYpZGVSX9yLZWJhgdmGW799zHLjwuTX7MiXGRWfgVhlkVKxM7mOWHy0fNSxDO3Rk7DaX4ggoAOVpl2254lSDwbQeCEWUNs3E0QdCCFjmDHhH8B6MXEdl+OdFvwGu4I1dzHzMV9+1HM0V8KtxOvDivpVgh10/c4B1pTNLb1sb0lwaVLVvGTBZczsqqUzt4kLV099BglqCJBZXkhmmkTxEppT76VdZ77E+3BCfh9f9jG/C9P4IZfbBiAtv/3BPKBwVItURKuwiXrzc9v5NfvdVBXbHPrV/ehwtD+Y6vimvTwE40Eye4Ibtv8ozsoO+4bmJVluGYp3V4JpvARKuTYDRbCcbo9xpaVsWrqBWAJgqRk6y8a6Srq4pDz9qb60pXgeyS7A9J6KSDoC5J0JnsZg0/7Q9egF5ZTccoszIoxoJl0//5HFE86En/dUtx3Msq//7Ust6K5EKigE80oiVAcAi36XE4roIxVkNfyaOhVLmycrYtyXOu3ohR49bdHRanQveAJrMqvhJDp+qHFLpUecBsIXptLyRG305XUyKvanSt/+DIy8NB0Ez1eQpDqhMBF1y2wi3dZcaz+cH0XN/AJlMLxFU2uxMxQGNqfuYOai27Eqjk3mg/t3Rbhbl8eFl/yJmLVTgGIxq/fTaJfaTqae0fNRPpdyJ6XEHwDFfQMwN+FQBGj97XnaX86RGtUn70Y18ijx9epOGUWmhUnSHZHfsuDO9BOykPPL+MPDSmSniLPhEPr4nipcF5KpyWeXkRcT6JpPgWHnUmSPAJX8beS5p2pPn/xmoOiz/iBokcfmrjt6roDu0L4gcLR6kjvtggrBqkeyZsPtZHuXsvnpx+AIyVdqcGcZgWBoluvJd900bQ09vibEcJAuS1gFIfoNBUgjPIBik1UNI+jlMAe/R2crfOjoqEwa5BuI1blGbjrV2GO/CJOxoM87JDPQ6s+HNn8SvTa93UUOrHR++P1eojxV6LH4gS9XTQ/eTtVp12dE45vaor6TpeC/HLyLZ33O1LEpUt5nsWv3+vg1+91ZH1nxjHjuff0fXFSAWP/ewz8z5awQaeRlTCblsbW7u1cfP/1bO9oYmRZDT+4YEGUZA/+3Jauei6677roczefMYtlq1ex4LSrsHcxq77hCOMTm7/NmDEDpRQHHXQQV199NY2NjdTVDSS0ZWVlSCnp7OykpKTkY9zTgejvkBsxA0+Uoms66d40yncIgoD8eGzId2zNQWYeSuvdBo5IJvELqugbMYFj0r/nT+kvM7K6iGff7ebig6AsMNH08AHqqfCmrKjMxxMmVlfTkO0Px3DkiiDtgmai2RKv49fY4xYh0FEEeG3PYZR8BrQ8pNcFSDDKsEZOCwXegh6QDgoLe9ziMJFAoqQMYWiaifSdDJf8yaxFppP+a8Tphmwf3ui1Fst0je7NLEZrkc5WvLanMEu/gOz+TVaSLoM0KA+v6X7MyrOwqmcgDBthlSD7OtHyCpHJVlBJrMopKKcb9/XbQg7aHpMxjrov5I3rFqCg7nieXXUXR379cpatfBqA7S0JAim5d9b5aEIw98KTuf2nL3DBlz+LYxUjdZuXNrRxy2kTuTqjwlpXbHPLqRPJN2DZqROZ/mT49/Y+l2JdcPuxe6AMjaDPo+OlBpIn7oan6xiaICYUTamA6U+si7Y190t70tbnsbahh6se/8t/NOdcaiHvtfPFVVR+9XpaH1+As3UdrY/fTOy079KXb1GWr+G6oT9rLs5d4Kmsznn52CL2/uoe/K4+TdKLkWfGmVRi8Pbj7zHp9N3xlE+tbdO28urIPq3p/mkYpbXUXHgX+bt/KuKgQ7iwU72t2CNm4dQPKiyNmDWAUOnXbgh6wCiNkvdcVkBe++qoeOY13IFZ+40BXnKqHW/TC5gjj8ZZMycUktt7MlrdZSB0FAJ7xBVIX+AnejGrr4lE8CKV9T+vjH5LpZoRykfX45hxC/Kq0TJJWWLLelZ+8+Ssjnle1e67dFJuGRaGXkggJbV5AkMrpPjcpTQ/OCO05AsMMMsijQvpJ7ILnS0/wRp51QDPvGklZvVkMOvC7ygP6WwJLR8zRUww0IwC0Gyk14rX+gRm1RTan14KhPZMzQ/PovaiO2jrc+nVCkn/+LoIsTHgsd1vUybR0w6Hj4iFOgVK4aWcrKLezhApfzN2ovos/ezv9osA/kPbHo5PRAQerF62cegbf+Px4weKrqSJbdnkx/pAeaigE7/zZazKE1HKRSkVJusqAATSz/DChUDphZku+SB0iFaAu2YB5h5n4WaScchowKyZg33kMgi+EcLiu7pofvDCSPfDHr0v5cdPpfHBG6J7ROSV5bYQ1Ex+traJNVu6uOfM/SnLNynJWJutPHt/lv/6fdY2hHSgumIbKRVTfrIuSztmZNwYwuvuDXqjZBxge0cTF99/PU9MvT0rye4NeqNkvP9z33psMfNOmYovfXZOHhmOf+f4RCbkDz/8MLW1tbiuy4033sj8+fP5whe+8C9vt7y84APYu52HHoRc79KqAircPXln2xuknSICJ1QSLcrPwzT0rO9Yuo+S4ffaHR8dD7fTJHXw5xjb+SZHpt7lN2WTSDb+ll9u1LlolIWhp4nHTIQqBgU36InVAAAgAElEQVTj9xhFd9KhsLeZyspCPq74OH/7/9g778CoyqyN/26dlmRm0hMCoSMIKIiKgq6FD8uqCIgVERBEWXBFBXHVFbECCqjoLkVAwYIVG2vdXdeKBZWmdAghIZPMTJKZTLlzy/fHkElCoqu7KOrm+WsymXvve+89bznvec5zDvf1f4pr/5T2Gq+qpmLVTLLOm4SaeSrxnY1Ep4pnYplO/GuW4j3lbBK++xGUTNSCq4mX3IOV2I+UdTFy1plYWnmScmbGQMkDLEzDRJRtmJYrJU6kZJ0BNHfAG9fhPfhvM7KZ+K7p2DvNb3Dg47uxd5yDpVcn6es1n6N6jscyslBcZ5D4cgFW8FuAZGkqUQHTQN/8V5TuV6J9el8DZbLfDPZVRkl3qdji1VQ+OT01UQ8872ZMQWDdtqQ+Q1GuF1mWePn9dfQ7ogOeNCcjTj2WZa+/z2WDT6BTmxxO7ZrN01/sY+rpnfC6FLwOhRe/KmPU8e3onONk6WVHpSjn+94tYes7pUBSgb3NiM6MbiQGN2/4kfz1gz1N8tFnrNnK1NM7MeWAw28KAvmHuc81xqG21+/rU5bpIv+KB9j/+A0E3nz0QKS6HYZkIyq6aJ/uRGxU0cLyWAyedgymbiHKAvY0lVhYaxI5P/L8TnxZrRNJJBdRkYTFl9U6R57WFkkQcSg2bDGtxbq1giwhZWQ2i7YImGhfL07W5bVlJDeDvl6MetztyQ2rA9UCtP1PoOSPaXC4fU83Y4io+ZeTqHq5aVpJ8CNUzwmgm6jFZxP/+Kbk4jJagfbRn5L00uIJoKRTtnACsreAgnGPYggFqO3nY9b5EG3pSXX4A/0GDtA9RRnRYeOjfVFsksiRuTaiwQqWHXDGIak8/Ngfz2fKk5+QmdM0h/+HvsvDhR9jr6ZpAjZ8VdXMX/Umdw7tQ3DtSgrGPIgRrQU9gWWCoAJIaHvuRC2egdrmD4i2dphaOQn/G8iekxo2Ns0YllGTpLSXPZRk7tRHztvfgakHESUX0S2jku+/eCb7l9/WpF16sByjLkjbzDzK9Bzc4x7DJRmIsork8uIQ/41MkOO/1wKI1sabMVBcmXZEWSAn85f33uvxS7TJ78OPsddDfW/f9Y4Vm0RGxr+P1pqGHYxKEv43D4x7a5Hd/UE0sfRQkv3TSKBS2/cwSs6wJhvySG4Sa2ckq0Eo6S2KtGGa6AmR8kVXpdKZ6hEv2Yj/jQUUXLkAI1SFqUUxUFEOouPHet7C1IdfY/KIwWCBJEBEM7nxpYb5+e5zj2Du33fir9OYN6wHc/+xs5l2zIpRfchOU5u0cXdVbcrJrkdpYD+GqTd5Z9/1u+w0L4okk+NNR9d1KkJ+EoaOIsnkpWchy79Il64VPxC/yLdXUJAUy1FVlUsvvZRrrrmGUaNGUVbWIFoWCAQQRfFHRcf9/nCzHO5DhZycdAL7wyhOmb0xHdXmYN22f5DpOBspkXTIZVkioTdNElVkA0lJ7hprBygrWlkUK5qgpt2FjKidT8I8gbdcXh5fH2bUAAlVjhCN6YCAoSi0aZvN3k92kBMsp7IyxOFATk76Ybv24b7+wdc+VJPhT2mv6aKMEfJTtuBqbO16pqi+oppO+fI7yDprMqHPXsGI1JA5ZC5aRCNaJSN77sfpERAlKZkrKTnA0hEECRMVPfhPVM/xaL7VSeGierrs/mT0rV5Yrf6zrf0daPtXNPzd9ia0/YtT7TzYYa/PUxeUHCzLQknrixkJkfjszma73AgCSDYSwYqGkj+9/pCczPUYVb4adlSZ9O1cmHLGIbnIDb9yL57RjwBJZ3zxTaNJs6mcf3Jfxt23nFJfkKJcL/dPuojZK9dw25jziKBxarccHnh3JxvKQhS6bUw9vRPxhMH2cDzlSBe6bcwb2oNCX5Sy9X46nduBa/+2panq+gubmHp6pybUuPryKpDclRfgkNj8L9Fef0h/lmyF5F71WIoCG8aJoSfptX5/3XcfaEA4rjWLnNs9NiL+pqrPkYSFPcuGYZnIuoosK03U1iFZKsdCQnJlNYu2WHosVZe3HoIjD0GwiFe+hFp4FRZ2bG3+gGWBJajYOz+YFAkTVGydHgC9BsuoxTLjGME1mOF1Sapz6YKkCrehEomJOG1Si4tUOSMT7YAAqh4sRxAEKhZdhZSeRdaZk/B/uIDsQZdi1exILUrFPncQqFT4MpGkN/fKU1lXHuVIRWuxHJAWi33n+zqUY/OhdDR+jL1mZacRi2mMu28ZD44/C211MsrmOelSyhZOwNauJ/kjb8fChaVXJ/Uy9CDavkeSm5n5V2IGX0dPlKPkXYZgGQgImKYGshtbu1tBEJNU3TZXYxpxEhXLUAuuwt7+YQzRTeXuMoxQUyHbZMm+IEJaNl/6BcDOoA5OYqEoxL6nDxxCfBcDxZ6mHtY1wffh51ovHA57/Snu7bvecVTXCVdqP+gcmW4bas5wtMoXkhv0Rh3IWQiSgaB4wDLBTKCVLz7ArnsRNWfogU3JALaO81PK6qna5wdHthGpDNY0SWdqPFYbIT9axU4qVkwFoPCaJZjeNggnzCdQXU1ZoI4757/Luq0lbNxVxlMzrkYWSKWXQXIevuXVb1lyaW92VkXIdCrNKOxlNXHiCSP1HurzxgFWXDWbeW8uZ92eJBW/KDMfSZSbvDNZkinKzG/ilBdl5pOTkYlLTKO6pu4HUd9/LH5tm1S/NfziHPJIJIJhGKSnp2NZFmvWrKF79+707NmTWCzG559/Tr9+/XjmmWc488wzD3dzmyBeq6GmKfhlhaqaMqpryhFEE5uZdMgdNhuxRENnEbCw2QQUW5IiZunJ/9m8lSTeK0U4qxPxwtMYydtsK+jJtu1B9ta66OKsRpAkLENHx0l2tsHGSBwzHMAyDQRRat64VrSiEeJyBrmXzcb35LQU1TfngtsIvDkXI+RPTWae069i7dOVlK5vWAi6Mu30n3wEj619ihv/71wkoxrLqE7ufudfQUmdhwz3cDKyz09GCMseTeWyKgU3k6hJYO/8OFaknMSWl1EKz0DIHJEsTSJ7mtLUiu9Aq1jR9G9/ANQMVLsElgiqG/W4GWifzmiUTzYTU1CpfHkeRshP3tBkSav6UkH6EdcTNlRWvv85x3crajHqqcoS/3pkOoosYVcV1m0rIc1p554Jw3HaVKrDEWavXIOvOoS/Jkxmlp0/vfItCy/uxZ9e3ZJyyjE06uqi3HByEcs/r2BDeZgpL21m+aW96TvcIiQLLaque11No1f15VXq6evC/zjl8z+i1zY5vmnJGSlNxVlrpCLkAE5FwO5SiCRCRPU4TkUld8Tt+J67o4EaPOJ2QlUa1ZUWHY6bmap3KzjywJGPcvTNJL66t0FMqNc0tJooav4otLJFKHmXYhq1qQiRqXZEMCPEd9/QJDXDMmPYuyxK5pcjouZPIPjPFwlndUfKyMHuUVtcpIoOD9XvzQWSDpxeXZGi3PvfWID39HFYziKUE+cjYFFdHufzZT76XN6GSCS5gayKApGEhW5TWywHJErKd+kR/iYQqEtQGYpT6guSk2FPjReNNQwCb68g8/eTQFSxLAvUdtjazyS++89J0bai6xDVQsxoNQgCgi0D6ipJfPMAQlZvlM7nQiKAWT+WZo8gsX4RcocLidVVI3nyyDtAk6+3vZwLbqPmw1XYzr4JSNorP7Pg43eVbhJE4WdtRyt+Ovwn5bkOhqi4MfQslOxzDqS++UGwYegORL0WweFCEORkdQHTQsk+JyUWa2t/J4ntq1H7TEX7cg76tqdR+9yUoq0LjjzUY2dQHtTYFwiTf1A6U+P+EnjzUaBhMwvAkh1c8/AbrNtakmpvqS+IvyaEJLVcFcW0YNJzm3j5qj4tirQiGMSlCBlKGhWRSkr8+3Gqdpyqnb9ccTu+2gDVkRDtsvKTwm6NnmWalMaSMXc2cbgfG3sXmTYP8ZhJXIr8IOp7K35d+MU55H6/n8mTJ2MYBqZp0qlTJ26//XZEUWT27NncfvvtxONx2rRpw5w5cw53c5sgHkqgdHATlyQ+/fYt0m3pBOMmYiKKXVUPOMoNnU4i2YEVRU1+r1kgg5IZQNyyDWN7OpFOZ6KGtnF1x23csN3JOyVeuhaUIskmugGa6SDNHiAc05IL9Do/pOcengfQil8NNM1EzigmZ+xCZFEnUVWSEgLKu/x+BJeX7KsWURqP0f2sPIKldamd8f7ju7P4k1Wc02cwhpiPIDoRlByiOUewtsJO3DDpU+Dlg/I4R7sFtMSVeDtdg161h4qn5hEv2YitXU/yhv0Rq/JLtJI3Uo5K8N0nSO93LbacIvSaKhIhCaVgEnruBKyqvZQvvzd1fNbZf0TNKUCwTEzFi23gfDASmLpO8KOXSes9GCPkTwp+vfQwWYMmIKe5kdwFxKNgt6ncNDIPX22oxahn3ICte/fz3D8+47LBJ+C0qdy17BXuHD+UCXOeaBIlX/b6+1xy7hmU1cQRBIHJv2vPhzv86JFaht+3LPXbB667nL98ChvKwyRMCxEDSZBbnNCzXQ01TwvdNuac3x1BgKmnd+Lpz/cxfVCnw2E6vykYRoNarRSNc1yhg09TolcCx7dxENVqqYmHEAWBoCXhPSDmJqoOTC2KqXrZ/s8aeg6Q8L2yBM/xE5AzMhFdWVToGXgtE/GAmJAZi2LoClUv3oG9Qx8yT5uEhYAgZx6IEFkk9peAw4vgvBZbXjGGIRCOytgdIFlxtPJd+F9fhBHyk3PZLKrLK3jnL3dw+sSZFB5zB+YXtzfZmAp++FKqfFvuyDn4X74vdf/xko3sX3YdbW96lVotA9kQeHPeJwBoIQ2nIhNJWGhm8nnsSmQwat5qnpjyv6W6rhkmwahBUa6XytoY+QfGi8aL/tBnryA63HhOvTwZ9cPAktzYOyUZD2ZoL/Gv7oNYELnnjdR8+DaeE4cid/0DQloWW8sStMnMwqlmouaMx4hGsTyD2P9ccpM0Y9xjbI7n0HP8XzBCVRjhIDUfriLj9Al8FnY0E2n7OdG4H7WqPv828d++Y0EQCcUycdkdCIKOJckk4i5ERcSMhaGqCtntBt2P4MxBtBVja3cLerCKREjEUo4AZxHqcfdhJmJYDjdq/zkYdQGM2gC+VxdhHzCa59du48ZhdxF78dZUOpOc1Q5BEPC/Pj81FtZvZqUfcw7+127jjkvv4twZT6TaW5TrxV8TxutOb3F+FoV6pprB3GHduf7Fbxr0Xs5qy4RlU/GF/CwdexeGaXLz83NTzvX8S27mntcW4gsFeGzsXXCQvFRCM2mXUcRLkx8mYTSoscdjyQevm3qLlPbW/PJfNwTL+h+pn8NPT1l/fuq/sJ3fFbwObl45ij55R7EpOghh92cIiSinH9e3yS6YK7qD6YNeomKPA4+h86f3w0xVElQc0Yva6lwCe87GMbIzopzAu2kuU95y0qNdlL9e8DVV+08kWquSJpaRKW/nlZf2c2yek7RLHkJs0+snucd/d/+tlPWGvw8Ffkp7hSQNTdQsjLiBEQ6i2iy0uIAtKwtDFUmIUeJVBrve2U/H/oXY0xXkNImQFEaUnDhtLhRJ4KO9EfoU2DEtkAUBzbTYWqURN0xOLLSxf/9+2rklSmcPaXJ9W7ueFFxxH5ahoRsmmDJEa5KOTiKGXl1B6IvXcJx9I4FAAGvpFc3uIf/G1Vy75E3mjDqd0IrJzZzq7PNvYv+y65p8FzrnXtw5hRimiWlZBGpCFMthIi/cktpJtw+7i/FL/omvOsTim0Zzy8IXmDjsNGYsfZmBvTpzzbDTCNTW4a8J89w/PmPskNP5y6d+/JEESy7tzbin1rN4RDcuuGUBpb5g6vpFuV7uunYMD/yrNCnKltCRJBGfZjaltA/rQb5DImYJGFZy/fPAuwerrAuHRGX9cNmrJInIltBixOVw9WdJEpMiZgdErwwjxp7APh58awWTB46knbcQmyoTKatEtVnouogupOPOTOB7bFwz+/OMX0KZZqejrQ6zOkmjrH7v8aQQGND2xhcof2xSi3YbFtMJOfKQZZkFz7/DBxu2M2/yRYixWnoUZeEv2cqLD/2ZPesb6oOfM+sVTEGnMNOFy5lGSLcRj0ZxSiYOm4IvGML+0o3Nrue68jEkVyaKluDd+7+kLhAjq30GfcYeyZfVeiqHfF15FFWETmotTkFP1iNXM75X0O23QFkXHDbuemMrI3qks+zld7lzaJ8Ubd3Z43dknX0tZrQWI1KDXNCVxN5NSM6M1PtW8zqSdeZVGCEfRm0A/zvLU45B9vk3YeV2pdayEY7pFNlilM8Z0qwNbW96Fc2Zi4CJFA0imDqSaiMup2MK4gGRNu0HjQnf1/cOFQ73muD70EpZ/3khSSIOWSYRN1q0N0PUqSrfR5c22UjEseJh9JpK/O8sxwj5yTrnevyvzaVg/F/YO/t8AArGPULlC3c1H3PHLiQmO6n2leOyK6TZFOqiMXRRpSBNwQgnN7NCX7yGe8BFBN58lHjJRvJvXM3ptyxvttF+5fCzkVQbM9ZsTc3PDwztgYWFKICmlTH/7RVcfPxwuuR3Yk/VXh742yOs27MJSFLN773gei5fNC3VzqLMfO44fxJXLr2Vosz8ZGTbaB7Z/q53GZciDFswuRml/bvO80PRSlk/vPjFRch/rdDjBgmbjJTpJMdKUB2tRlUyMSKQiIRpk+Vu4owD2CN7AZAlkVhCpUCwMBAwRBlbegWOtC3EXlWwj+hKTberOGHjYr7cl8xxVGxRoqhoVrIDtT2iPQR9RMu+xXUYHPJW/PpgGCaKKvPegvUHibWUceqUo1FMJ58s/4q6QIx9Byjrrkw7p/6pHxEz6XhbJPNsN/s0euTa+KK8Ibp4bIGdB99+jHP7nIIvQov5XLHS7SmlavfQP4OiUvPkH1OOseui2ewMSwhRncwWothb9/l5c+1mbhnWH6sF2rmS3S51XdlbgHr+TOa/9CkXnHosM5a+zIrbxlOQ7UWzPORetQRMHd0SCRoyM8cPJd1pJxyN46sO8eiLf+f+SRdx44JVbN1bwXUXDaZL2zxuGzOEZZ9X4o8kmPn7bpiWRVlNHF03mjjjkKTBeR0S84b1QDWTEQfDMMlVRZZd2jsl+qaaJgnNQAJUScSQJaYN6sSNp3dCFiDXbcdfFT7kNvFz4btqF0su6bCWcjMMEyPc0BdMJY4Ul5k/9E9YCUhEdAwL3nlkZ5Pjzr6+Q8tpDxgIgGCa+J6d0cx+EzW+ZrTKvMvnYLqy2FseYubDq/BVh7h/0kVs3VuBLMsMvfspXp8xkqXjzmhyvUDZHmQMJjyyJqlt8PDz+KpDPHv3JCKCiiYKuLJdZF1+P75GtGfvZfezLuwgXhNlQJEjlSvq313LN89vY8Dl3bGkZC2FAUWOZGKAZW9w/n7DkfF6ZLoUrh5YzLqSam6/8nwMIPuqxSimRqKqBN+zt2OE/LhG3EOaoeN/bW6zsc6IjWHv/LFNzls/RiWcHraWJwjGBDxZess6BaJENBQ58E0yVSMnPZ3gj3S+fql9rxW/TdTb21sLv25mb2ChYxDXDMY9tJqTenfmz+cdReCp6c3SMtLOuxkDMdU3BEluccytqa1lfyJOZpoHJeqj5pmGygPhSx/A5U6KT6Yfc07KGZe9BYiKjWdmXk1lMJmCtuz19xk75HQe/aiM7DSVBRf2JBhJENUMvM6GdLKQkMvFxw/n4bcXc8u5E7jsr9c2aVNpIElVP/g7jzMj9fnHRrZborQvGXNnM+p7K35daHXIDxHC/ijy0XkIpoUZqca0LCzRi2UahEIhsjoWNTvGHkvmq6iigCaoFAoWIUk+oOgKGW0/Q9uSjfaGCGcdQdd+Z/Hak2+hmwJxMUmf0SwXFgJtOuSh+Suo2fwBrmNH/Hw33opfNSzDauKMZ7XPoMfgYiRBwNCtFsvZiKKIfGANHjeSzncwZrDZF6dXnh2bJGCXBSLRGkYNHEIkoePx5MLI+wmsbHAEckfcjv+NBcCBifSlmQiDb8A6dTJ5xd3QRJVaMZ1iu0y1YmE/QENrXNZn8oNvADShkdZD9hawq6qOovGL8QerKQ/WsfilL7l2xP/hSXPy3J0T0UmmkYRjGkFdZ96qNxl50pHkuh3kuN28+eU3nHbMkSyZPppx9y1n9so13DNhOMX5WZRVVjPlwafxVYd4euYfGHREDl6nwpKPSih021AUiaJcb7MIeb7bjkMUDyo3ZCIZJhKAQQONuj56/szGJtHzXPchM4HDgu+qXXzqlKN/MbnIkiQihBT879aRfUqctSu/pS4Q43cTezdTHNbiQotOVI0G7Wwx/K/Oa+54XzaLqldmI6VlUTDuUXRLwBAkwgZo1TUUumRuHnk2965cw40LVnHPhOHkeNIpyvUSCMdbzOUuyM1mxtghzF65JpUPGU8YbKzRcSoCfQpcaM6OOMYuwYaOTbXxSdBGMJbs0BYgHJQrqsV/WNT1twxREMhLV+idLTLi1kdTUbRXZ12LLUsh66K70S2BcMKgTjPIOP8WalffnXrXGeffgiHILdqIUVOJojiJG8kl+eY6J30vu5/gkzc2GevqzIYSZv8Nfg19rxW/HXyXvZ12w9HsCVbjC9by5Fsfpza7LyupYMX1j2DWBVHsLqLROuo6ncLrC+dw3vQF5Iy8n8qVN7Yo2iZ7C0hPT2fSA8/z+A0XUPvkrU3EWkNP3YB65SKQ7KlNM9lbQM7IOUTldOosjZxMD7mZbka5s/jL2v34Iwn+eGpHbn99K/46jTnnd+cfWyrpXeRm6uoGmvqfh9yKafhaFGOLaE3XUUWZ+VRHalOfZVHmx3S+ekr7i5MeRjcbKO3/jaBbKw4/Wh3yQ4RgVRSpWxbpWoJAbXIBrpOOEQ1jWRbuNFezY+yxfYCCYulEhHTaihY+QcJuJnumKCXI7PR3AtsGo72jUnjqscjKe5TVOolYGskziiSsNDxpBjtiCWL7vvnZ7rkVvwGIpJyLrPYZHHVeR9au/Ja+F3RBlIUWS52ARX2J2T3VGn0KHHxZHiUYM9hQEeP4Nk7i4Rg2xY7sTGdflUYbQeaLRCHHXb0MU9dQRQHf0zenqLuQnDCLijqgmRblCQd3vFuKP1LCoxf2JCY5sHmLyb5qMaJpsC9s4lfTyPGms2T6aFxeT2qibhwNn7z0Le6eMByXIwsrpnDp4BPI8WZgmCYOu4NazcQSLBw2i9lPvs51g7rz4q0Xp/JjR897iZnLX6YyGOaeCcPpUJBNqS/IlAefbiIAI2LRLtPB0o9L+HRPDTPO7oqi2nls+hiubJRD/tj0MUiyQa1ehyz9+0lUE8VULXI4oMD+4mZWjOpzaO3g58Z31C7+JeSfSpJAjCiK5eD9hZvoe0GXlDMOsHHNbvqP6s4nT3yTiviITg+5I+/H18j+HBfOotJykWGGiWx+DyOcpF7WU5nF9EzcF8zEkm3U6QkSsRhOm8zqe6ew4e+rySws5oJ7nmbmuPP585LVdCjIpi4aZ8Vt43HaFDo/uJqlf2zI5R5211NUxgTG3bc8dS9FuV6cqsxJ7ZxopsWOQJxij8pavwOnItArz04wlhQdrRcEa80Hbhl1kRjjDvRlgFxPOuWBWq6atZyTenXiD6d1451FdzL4ij+SW9Qe9Yr5xOrChPwVxG0eglGBgpGz8a2c1iT653/jYbIvuZfj2nj4dF+UYMxic1objrp6GYauoSoqmuzAiByil/EL7nut+A3iIHvLbJ9Ou0EF1CSS/Wn+tZfw5tpNVAZDzJl4IYU5HrYGwrgTcZ6eOCQ1vl183yrGzX2e+yYOxzznXrILssi+bDZVT05rsnG1TxOZOOw0tFi0ZdaSYFCLgvuKBYTjCfZV1nDnio+4Y3wBmgHpNht2RcQREbjzzA4ImJhYzBrSjS0VEZZ8VMJ1p3bkmlUbmszLM98o5Zb/czP/kpu57ul7m4ix2RU15ag3ziH/byLbCc3EhjMZWTdojYz/BtDqkB8ilEZNhAwRj25QGk/ufEUNJ2I8SfVNdzk4OP3HnvABbVCtBHFToVCwWGtC50a/k21hsrq9Tk3JABKvGXQuLmZX1VaK82sIm51IE+NopgunUUlCdqFqIcIBH2mZrcJurfj30AUrRVHtMbg45Xg43CpfPLeN40cekfrOlWnnpAk9CUZqcNmT5QY7Z9rYHmiIjNtkASMaxzBMVKeDj0uj9MqzU5cwiRvwadBBj1wPZjzQYgmfhG8ngdfm4r1oNgLJia46pjN6xdcUum0suOBIHJKBYk9SgW+67GxG3bWEUl+QM48/kjlj/0qkLkypP8zdS/+FrzpEZXWIfYlgylF5/q6JXPfQ0yyZPgZRsbGjJk6HTBsjTzoy5YxDkgK8fMpQRt72DJff/xyj7lxCUa6XeyYMb+KMF+V6QRAoCUQZ078dZ/aI8/Tn+5g2qBPFHi8v3T2JhGGgyBKhRDXnPTTlB5cq0U2rRYXXhGH+ugfvRhtB9XBl2kHkP3IM/tOcWFURwbQQDAFREhGAmKFx68vzmfa7cdQFYqguuUk7/btr+Wr1Dgbd2JcwFlrCpDoYJ2YvxLz0LxS6RDQktoUUVn60l7tPy0kpcdeX25G9BWSPX8xlD7zEwrEnp/KRo94Chk64mdqqcvasX8vzf7qEQXc+z3UXDUYSRa68bxm5nvRkukRRW65c9E8ikTpcLhfL//41I8/smmJlFOV6WXLzWEoiEvsrIwdE6px8tT+GUxE4ro2Tb6uS9+VUhMMmCPZrgWGYTdguE4edxlWzkjmnZ/fpwDuL7mTohKmEX7mXffUOwsg5CB16seKf6+nWLkF219yUIKARqU0JaUZNCUWAE9s60QyLuGHxVbXAEVkeBE1Hix/C2PUh7nutaMX3opG9ZbZPJ+/sXMY+tJz5115CqS9IdThCUa6XdVtLuGTGQvp2bcd1Fw2moG1nrln2PtU1tUkmXnY+D13fCVmW2BoIMalqU88AACAASURBVO7mx8j1pHPLiBnkZNgJRHVqow7OuvF+inK9/G3m6BYj6Imq3dQcSJNTz5/J3c99wLqtJdw6ZgijV2yi0G3jkRFHYjcjXPLnhs30JdPHUOjO4Lxe+dTG9BbnZYfqJMNubybGBqSi2YokI4sSCy6/rTWy3YomEA93A34LMCyLgNOGuacGEYuaWNIhD+k21EQYSRSxq00zRKx4GIeUzAcTLRMjYeHC4uuESZ3RMPnuiMR4smofng7vki6+SXFQYkOpSltXFasdZ+PLO4mwXIhoxUkrKsbrsrHpjcd/vptvxa8ahmEiuCQGTzsGTxtXKlJucynEajW+fmUnfS/owulT+tDv4q7YXAoep5v1FXF0y8IpQ+88O+mqiF0W0MIxNO2A/QrJUkmqKPBtZTKSHjdMNvvixNUMHBfOQvYWAKSiRdXvPY4eLKdu1TSu6++l0G0jWJcAIMup4PP7GXbzwwy4+m6G3/IIVTVhcj1JHYU31m7irNuWsSVoMvzup/BVh5g7+WK86S4effHvQNJ5rg5HKPUFGXffMkr2lXPrQ8sAyHU7WqyxnJnW0HdLfUE6FGQnnfAD51s8fQwORzJH7NbXtjDzb1sZP6AY4gbRoIZLVHFIKoYYZ+TiaeSlZfHsmPk8ccksYiGNuNU0WiVJIjZRwoaEIgrJEiqNUOi2oUi/7qG7fiMoybgglVeoCz+ekpvKiZ33Fa/d/gn/mPcVVp2B9B3PSJIEElIMXYqhh3S+enYHNfsiRINxLMvCCsPcc28m15PFoOv7YE9T+d3E3mS1z0idw5ahUBpLMPqp9Zyz5Asmv7MNU5WY+No+xr9WwRdVEqoiM21QJ3yGk+yRc5rYetZlc9hTq3PHpaeknHFIRnDCr9zLedfcAiTtL02V6FCQzR3LXibXk860kWfzp4UvcMKEe7jg7pX4Eip/fvLvjDxzINkeNy/cM5mPF97Ci/dei2F3U+y1cXKxkxPbOlFFi36Fdga2c2ITLHrn2fm/ji5ObGPHisX/56npLUGSRKI1cRRZSvV7AE+aM+WgZ6bZOGXEWMKv3NvkXfpWTqU2FGbOU28xY+nL7KkxENNz8T07g4oVU5NVLUY9gOryIsQSiLqBXRbIUEU6CRafLviKd+5f9732/GNxKPteK1rx79DY3toNKuCah1Y0ccTrtVnq+5avOoRdVYjGdVZ+E6fE8KA5stGR0CU7iz+toDg/yVRbt7WE4Xc/xck3LeXcGU/gdNjp27Udpb4gU5e9Re5B427uiNsJvrsk2a5gOdrqP3PLiIEU5XqRpGS54LKaOHurarlqVgMbpn694BR15ry7g6qw1uK8XODOJMeeg6o7cVkZ2AwnCc1MRrON5Heq7kTUbE3+34pWQGuE/JBgfwxMWUTaHoDcPGpiNQBUxyWMSC3Z3gz0g4VvqnbhcB54/CYYEQ2AjYaIHKhlSE5ycLpjVxmGBad7M2iTvZ1jrHI+LHMiCRa+is34Op6Jlp5Jfsk6xE5HIPi2s+vt5fQZOhHV0Zwm34pWHAzDMHFk2qj1R3Bl2ukxuJhobZyB43vyweKNvL9oA65MO8ePPILPn91Kv9FHEjdMPiyJps7hVAQGFDmaLOgFIfm9ZlopR7xXnh1VFIglEtz8rxjXnf8QPbJkjIrtKYEVSE6WeU6BGWd35eH3dgMwul8eU+Y1nSSvf/gZZowdkop+l/qCFOdn8Y8F05AlCZsscduSl1i3tSSlnDp75ZrUb+sX1fc8/gp3Xz6oxbzcQLhhJ7wo14uiKKy8YyKReAKvy0aNJrDys30MOaqAu87phmFZvPx1OecWerHHTWwZavKeTJ28tCzm//4WNq4ooSSwC1emnU4TZARXMqf8YMGlboOKmD+sB9cdpMCe6VLw/4qjmYeirm09fkxOrCQJlEXKeeCNZdzz+ylsfH033U5py9qV32LPUDn6/E4pKnqb3ln0GdoZLaIj2yWOGndkUnRPAEESGf3U100oi3P/sZN5w3ow5cXNTHlxczLSckFPNBMWbZEZPXYRdtHEFCXmv/Ipazfv4qkbhlHRAq0y3ZMNJO0vrBl4JYk3125iyfTR3LhgVZM+cOOCVcwYOwRREKhJwMZqkV55TvZUaxR7VFRRIG5YbPXH6ZVjI15ZxwcHCXoJrYJeLUKSRDggSNX3D71ZcOMVTLr/cUp9QSJxLcVGCITjFGbnU9vCuyx0O3lt7lSqIgaz3tvP7KFHkD3hMSRTB1nFsHn44KGviNVq9B/VHafXxt8f/KpJBPtQ5ngfyr7Xilb8O9Tb2+Bpx1AVCafGrsYiqQdrs9y34nV81SGev2cSCUFFFECRBKat/pYNZSHO6OJuUZ9lz34/E4edxrj7lvPG2k1Ex52Fdepk0lzpZOQU4H9+RrM0uQKviyXTx5DQYfEFXaiNm2Q5lRYFWUXLYvklvUEUUmN9/bw8f1gP0k0RCwtdimEBdhzfW4GiFa1ojF93mOUXANOy2BcF9oexY2JZUBOrRRQziOgm1YEq2uZmo+lNp1Jr/2bsLhnDFBAsASEUxhREVIeTfwVDWJaFZpopAdstkeTk3CGrjm2+pMJqG+c+Nrz8MfaKreiCDTlNwkIgWzV4bcVDBDWL/6Gqdq34L1G/k21PV7AsQLBS0fG+F3Th61d2sm+9HwHoW5DMQYWk0923wIHQ6FySJCKYFn0LHKk887hhsrY0yhflUWRRpCqSYOxLe9nsT6oSN54oZW8B3jQnT3++jw1lSRXhDJvY4iTpSWso81GU62Xb3gpOnTSbu5a/QiSmcdngE/jHgmncM2F4E7Gr+mg5wJtrN4E9g7EPJnN3gVQO+cr3N6V+P2/K5Ux/fSeTX9qGZHNy85pd3PfOTgZ0ymLSsxsZsuhzJj27kf/rmI3HLrPzkzJEKflkZFHmtjMnsnFFyUHO4ybUA0OxbAkpZymrfQZtemRTvnon807qyIuXHMWyi3uT75AQhcZP+9cJwzCJmwZxDOKm8Z87BD8iJzZGlAfeWMbYk4cTjkbp2L8wlZLRY3BxyhnPap9Bt1Pa8o+Hv+bz57bhkwTGPLOe3y/+nDGrNhAzzGaUxX9sC+B1KMw7qSNrJhzLQ6d2Zuvybwi8vZcRRxUSVjxUK1lUGGlcfMZA5l13GYKipiI49ZC9BYSqq1I55O6cNpSHdIpyvU2isvUo9QXJz3IjSlKqr+2p1uiUaWNDRYz3SyJsqIhxRJYNYomUfdU/pw8WbkzS/VvRDLIl8P6B5xUPaaxcX8ML90ziw7/+ie7tClh00+jkuPDyWuyenBbfZdySuGLVVm54dQf+SIJowuKbkMrQVXvZEbGBAMeM6II9Q+WTJ77BNFoW0zyUdPJD1vda0YofAMMwcWTYkCQxFQlft7Uk5Yg/fP1laAmdKQ8+zSUzFrJuawmlviDxUBy7CULCREDAX5cMXD36UXmq7wGpzfb5q95KrQeKcr2UR2VIz+GvN1zCvm0bWkyTczqdzFv1Jvurqrhr8bPc+tAyjEScM44/sslvi3K9SIJIuiDg1EzaOhQev+wo1lzVj+WX9CYrbmLGLdAFXJaTpe+/QFmkHElqHVtb8cPQGiH/L7E/BpoJ8Y/34SlO5orUxGtJc3RCr6tB13UKsjObHWft20Da8V4MQ0bGwOYPEnd76Sc6WF4eoELTSTRypndEYpyWmYEsQoYM1RGZY9x7udM6lg7vxzmiXzq5ka8oyyiiSweJlc/ORj3iJPJ7DqDQYZFv5zexiG/FT4f6nWyHS6GmrA49brDu+W3Ncg1NBDb5Yqlot2ZabPLF6Vdox+V2YJlJ9fZv1+ym05nt6eBVU/mRADHdQhIs5g0/kikvbGL+J0EeuHAW0WdvaiKINefjAJf0a8MWXx1lNXGiutXirngkrqU+z518MfeteJ2iXC+3jT6Pi2//K6W+IH27tmPayLPxVYdSv20cLS/K9bJ5Tzk9O3Rk/GPvI1s6oqwQt3m5Y1wHZow5H0WSEG12bhjkIViXoC6u46/TKKuJ8/B7u5l6eie8LoX8NBW5WsPUTPqc3xlIblCkSWl0yXayPfBZk+deF4g1LIgbOZeNc/rLGpedm3L0oX71v278iJzYhKlz4XFncsMzs5g7ZDrd0jumjmucK9742Xe7qAtT/ralSTS8JBil0G1r4pQXum1gwZZV2zj+8iNwKxKODIW2vXJwRnVsHpWAbvGH5zaR5VK56f864jPs5I6cQ9XKqQ3CRCPnEJZcTFz+AbqaztNfVTKgOIPF00dTGQy12Aey3GmUx+SUpkMyMg4D2zmxLLBMk0Q0jmkIrYJePwaN+mOaTWLCwGIqogY5NpGhtywg15POc3dOZH+ght0hgY4HiUxlj5zDovXJ8oSFbhv3D+3By1+Xc3SRh7KaODe+tJnlI49GzXMx8NqjiYc0ZJdKm97Z7FtflWpGa453K34LsEsKS6aPSYkj+qpD5HozcKgKM5a+3GxciwcTbH9uMz0GF6Nkqswb3oMpL2xmQ3mYhFDEPROG47SpVIcjzF65Bl91KEWFXzx9DKJi58HNMsNmv4XkVsm9fA6+FVObCL/OevEjRpx6LIoscf/ki7jx4VVMmL2cp2ZczaZdZakc8sXTRqNEBEzJ5KNlm4nVagwc3xNVFZBViXcXb0qxjvqP6s7EYy/hxjWzuWvYdSjYv+eptKIVSbQ65P8FEqZFSQRchkG0NIR6VBYAwWg1TlsxdZWVAHgy0oknGmZSy0jA/m/w5A7E1KUkDTKuUVOQRXE8mS+7OxZHOhBzlIDKhJ46vlumwYZ9To5puxtBMlhqz6RzaTf6tvsQ090Jb+1ecgvasGnWhdinP0Ws++/YF4W2TotcW6tj3orvhmGYaBK4su0YcaOZmvRJE3oCViraXQ+nIhA+YOOiILAjoNHp5DbseW8vHU5pi2ZBSDPZU63RPduOblqEdZGHL+yNQxaQJcgbt4RYdQTZ4+Sa1/axvjzM9qoofz6rKwUZNgJ1Gg9eP4o/zn2iQbX85jF4MtL58K+3IIgCIHLXxIvJsKuYsURqgq/fjZ8xdgjd2xcCFjOXvdKEyr76vS/I9qRz1azHm0zCHbIyCe6ro2ynn9zfFTF6xdcA9CpMZ8bZXZmxZisbykLMeXcHc37fDblaQxQEPmr03AZO6InpkpC+Q7neEs0UfbT+/weLiUGr89QSGgsTNn7eLeXEKqJMdpqX0sB+Zr27mCcum5163lqd3uKzl1xKs2j4wg9LmDu0B9e/1DSVIE2E/qO68+3fS+h9bkd6ntWBjX/bRbdT2vL+wo30vKonZTVxymrizHp7J9f+rj3u7GT1AEtPENMt5r71FYNPOBpnhptnPylj2BFpTJi1iFxPOreOOY/FN41m/AExsaJcL4umXQGI+GMmnQyD3jm2pIlYFvFw07xwWZRaBb1+DBr1R1GV8TgkMuwyiWgdpb4guZ506qJxbKrC+FnLyfWkc8eld9GtTRa6IKM7PVx0DAztU4hhJlNZBnTKSqXhlNXE0UyTr8o0jsq3IzpVtvjj9B7VA57YzL71Va053q34zcAwLAo9bl68+w/ohoksidilZE3vxo56Ua6Xv/7xckpeLyewO5RKmxtwU18WXtwLQRAor47hcrmarAeWTB+DO83Fk3dMJGpK3P3WdiYMaEcbj51d/iiPb4ox9arHEM0E63ft5/l3tjDk5GNSaUCNN+rDmsFd147B65DIctkoe7ucnVtKOPXao+kxuJj3F23gg8UbOWXSURgJqwnr6JMnvqHfxV0Ze+zwpJDb4XzorfjVQJoxY8aMw92InwvRqMahZHDvroNaHTz7qqn5NkjecbkIssjqb15Hl/sQ21dKpstGp7ZFGGaj1U75Jqxv3+bkoV2xiyZUJagO2Qh4s3AIsMZfS44qE7csNtVF6emyU5nQOSPL3eTaJ3Wuwh1Q+TxewA7BwbmZXxIW2yP69pDffyi7d+9k5+sL8UgW7u4nUqGJ+OIgCeCUQDiEjrnLZSNyIA/+cOBwXv/ga7tctu/59Q/HobbX70Pje7AsC0ERsdllbC6FDv3z6XpKER0GFGCqAkbCoG2mA1+dTsLkQH1jB99UauSnyawrj9I128bXfo1ir8q6J77BW+giw2PDFoiy4+09uDJU2uQ5MSoibHpyC2kZCnZ3Bv9cuB21OJuXdvgJxQ18IY3XNvqoqI1xZvdcvOlORpxyDGPPHsh5px5PWHCR4bLjctixRBkQMKp0St8uJb9nJn/7dAO1dcmJstxfw7qte7hkUH98dTpd22Rx2eD+9D+yE/NXvcXlZw3gugefTjnxtXUx3vtqC0NO6ssXj2/hmAu6UF0V44Py2lTbdgeiTDm1A9cMKOaMAjdZoohWl0htYgAkojr7Nwfo1L+AmBQjs3M6wa1hEtGkA9h7VHvEdAHBlEAUaNc7h/2bA+R28eLfVUsi2rAZ58q00+HEfGxO5ZDZ+y/RXn9Mf7YsC9Em0al/QdJOT8zHUoUWabiqqKCoIm9u/JAt+3dRrdVy8ZAzqPimmmBpmH4XdcW3tbrJs88/KpuPKkKEGildy6LAkHYeBhV6uLhPIYML3DhsMSzFwi7ZSMt24HDbeO/R9fQY3J7Pn9lKXSBGXq+s1Ll8IY1XN/p4e4ufI9vmgayiCyp5+flketxkpakclW9n1MxFlPqClPtrWPXup1TVhHhg8kWcO+BoTj66G6Ikkpnhoq0sIyVMTEziUQ1d05unLDWyr3r7GzihJ5Yq/GTpTYdybD5Utgo/0F5FgeLeOZRvDpDXPZOoTWb+33cwoIObN9duYOqlZ3HdQ08TrA1z14RhnHVCb1RnGoIjg1MXbmRY7wLkkIbsUBAFAacq8+j7e1JpOIVuG2f2yKM0ZNDBo6bGzi/3x+h/ciFdBxTQ/oTvtufDPe9+H1rbdhjslV/uc69vl2WBhIgiSkiISQaPBW6nneGnHsOY3w/k0kH9ybGlseOD8tQ4ddKEnuiKxJx3d9Cv2MO0l79hQ0Wc64cOZNRZAzjl+D6gOLlkxQZWfeXj9z3zWPzRXtZsrmRw9xwmPruR2pjOaT3bEjMlxs55irG/P4k/LXyhyZz/yaYd3HLFubTN8eCyVOSIxfYXdlG+0U8iqtPlpDZYWOz6ZD+JqE63U4pIRHV2fNSgH5GI6nQeWIhil7E7bEjWfxb7/Lnf5aG011b8eLRGyP9DhBIW5THIcwiEtlejuBREhwwW+CPVoLiI1Pg5vl8v4o2i2wDW9vcRFBvpLgMlmiAWtQi260BC05KqyjaF3TENjyzhkSWK7ArfRGKYloUoCHT06CzdkoYgwJE5lUxJ/Isn6o6lMpFObSJChpJORmQvJ10zh42vLearp++jctMHnDhlIRF3EdvDUBqBAodFni25uGxFKxrDMEyiB4fMGskg2ASTo/LtyEKSsr7ZFydumGimlVJWjyQsVI+Kf3ctXzzxDX0v6ML7izYA4NsSpN/FXfnXo+txZdpx56URDcU4aUIv1r++k1lnduWmN7amoo9Xn1jM14s20u/CLrjSVbAJ6HaFnWVR9oaiyZJOhQ60yiheu0z6wDY4RZVF11/BVXMbIt5Lpo1mzxtlZJ/SBs3p5Lp5K1L/q1dubYxSXxBTMOl7QRe0iM6OV3cx64KGtvnrNHJcKpuXbiawu5Y2vbPoO7zLd0a2bbKd2owQBZemk6EUUJsIo2do2AQ3CcwmgkuiIHDShF68v3DDv438/q/jh9bONgwLt+ph6di7Gbv0Fp769DUCkRrm/HEaCgqiLHDq9UdRHanlxPE9+GjxZnb/bQ+zR3Rj2gHaeqHbxuyzurHlue0Edicrargy7WSNsNPOXoDTltT4sEyrWbR999/2MHdkd65f/U3qXDPO7spTn+9j+NEFdM5x4ZEc7A0bOB0iUc1oZpNvrt3E+HN/x3UPPc3cyReTme4iQ1YxsEhgYSS+W/qrVdDrxyElSDX1GEzDRHcojBtQzHMbKlg07QriCZ1SX5Bn3v2MZ95tSEX516N/otBtQwvE+GD+VwAU9s6i4PyOqTzYQreNOUN7sKfWSIlfNh47TSBmGdDa3VvxPwDDsFCQUZJi55g2odk4lSbA1ScW8/A/d6XYaTe8uiM1jj50gHlS6LZRe2Aju/5zodvGzN93w7Qsqj8L8NjNY4nE4i3O+e3zsxH9JpsPpC3Vw5VpJxbS0CJ66m/LgviBSjCNf6fHDTLz3AhIGK2duBU/AK0O+X8A3bT4NgQ2EXJUi317QnjaZ4AFhmlSq6ej1CR3y4rycpvS1XUNa8f75PXoj8OWgIhAnact4aqGDl1sV/g6HCNLkWlrU8hWZHQLanQDryKjShAK2zBMyMgIkbHfxh+8H7Mp3J7+nm/4yOjBEVXrULtKHD38WtKKu7Hp5UWsnng8/S6ZSvHplxN2ZLE9GGNz1V6Eql2YlbuI+fYQr60iVhtAi4RQHWk4vLkU9hpIu36DcGbm8c7m9+ia04ajCjv/2wi7aZm8sv5djmt/FIXu1rrovyXEIxo2u41Py6JEElYqSr7ZF08tLp2KgBbSko7kVT3ZuGYXQGq3W01TOOeO/iCCJYEVstj9WTl9h3ZGVEQev/QoEqaJoFtItRpbajW+eHYbx4zoQrwugT1D5cR8O4IsgW4iJnTENAUQEGUB0zQJ/DPA0qtHI6dJuNLtlLxTzu6P91NbHuHIS7rw/N2TiCd0KsI6/qjRYn5uvDrBp4s287uJvYnXapQ9v4N5Z3VEcikQN8io01m3uxZXpp2ev+8AUsu0dERIaCY5jhwc+Q50UydNzG1Wh7Sxcym5xFbn6RAjoZkUOgtYPekRdDNZK1ZAIm4YoCeV2GsJM+PtBYwdMZy89Gyy3RLLL+uNboIsgBJNsLM26Vi5Mu0cfUUH7nhvAXe1vQ7NMrG7VUKVUVyZ9iZU+MDuWjLeLWXhRb3wRxME6xI8/fk+Lju2iGynQoYqsb4yRiRhsbVKo2uG0qJNtsn18txdExHikCapGJZFzNC/65ab4IduXrQiifoqFIFAHXZNJ9eAS/q2QRIsNC3e4vupiiSYN7QHpS/vBJI20uusDpSuq+SRM7piy7IT1WFvyEAzrBbHzp+NHtWKVvwC0dI4FQcKVJFpJ3dAskksvfQoTNPCFOCBd3ewoSyUcrwf/OcuCt025g7rgcehsOTS3qiyiFMU+fi13ZzXvz/Vrpb7rxkyWP/KnmYpeyeM7oGkCHy1ek9yXTO+J6IskJbtSI3x9TnktgwFQRHQEq2DbCt+GATrVyTDvWvXLqZPn051dTUej4dZs2bRvn37H3y83x/GNP+727Usi29CENCgh1tAC0TZ8OhGOp5RjFrkIhAJMmHNs2jb43TKz6J/rx5NFNbNb96C9x5m1AW/o9sQoEpge1UH9lc35Ci+HQjxVEU1AGdlZdDDaeOBvZXc2qGA7q5k9OW5bx1cftoeOngdbPyyM5IiYqeGo3K/ZWnJCZxV+iZV7S4g0qEXf93wOut2fsaZpUHEkh0gCKjODLS6mib3JqoOVHcOSkYWkj0NI1ZHrGoviZpKEERyTjifduddS3rHPkhYZCgCbgU8KrTPT6eqKtzkfO9t+5Rrn70Du2zj42nPIwo/nah/Tk46lZWhn+z8P+baOTnph+S8h8Jefyj+k+cnSeL/s3ff8VGU+QPHPzPPzNbUTSEEBESKImAvd5YTsXAogig/EWwUxVNRggVPOUXFU7CAYsMCljvLqYAFC6dyZzu5Oz0pgiBIDyFl03azbcrvj02WbBJQNA143q8XmmzJPrMz8zzzned5vg8OjxOU+Pzx70uiREyLozq62eCPcFiWEy0cw7JtbAGKyR4DSyHUeLZnq3a5Iez42mkKGGGTcFWUr15cgyvNQd/B3UjN9RCqiPDtog2Eq6K1c7ksPpu7EleagxMu7Y3QBOGqKEbUwuERfF4v8copV/VFuAXFpWGEV8eTIiitqEiax/bszVeQK1JxeTXC1fHhdl+9kDyn3pGiYxl2YrsA7KDZoktLNefx3h6P17Y8nyEelIcJJQL2+svXCKFgqFEcpo4Rs9haUcicz//C5EFXkO/piGna8XNDUYkGDFYu/jGxtFr94yHm0TBUBVVRcArQIxaarhLVNZbV3ujqniHQwpWMu29eUt6Ebpk+rJiNIhRM1Sa6h17xttYej1XYu+O1bhvqlkGry7ye3z+L7DOzGT9jftI81rysLLwqmFEb27KxTJvVf99M8doKTrjkUApXl9FrUPzmXVN156FZzp+1Lnxbnyd7IsvWNsdre/3em6tcddcJqqIQDRp8Nnclub0z6Hp2V2xNRVcVFGyCIQO314EqVBQUDNvGrSnoEQMzasWvS0ybTf4yrn5k10i5526trV8NG1VTwQbLtFFUEiummDELRSgoQonfyAV0VDDteC4bFaK29avb+9bel815vEp7b58KyC+77DIuuOAChg4dyltvvcWbb77Jiy+++LPf/2svGG3bZm01lEahm1chVdgUfrmDbUu3039sX6KKxeqda7nr/e8Ibd3AyLNOxULsen+0BuuVqzmuV2d+d4wb3wA37FBYXtaD6trllwC2hKPcuXEnABM6ZdHT7eCm9TsYk5/NGb40ADZWCGq81Vx6Qilf/+d4Yjb8Y5PBuP7fYwkH25ZHyTKibD9kAjf8MJegESbXncFdPc6i7McVxGqqcaX78GTk4krPwp2Zh8jIA1XFRqE6FCUYjpCb5qF6xwYKv/6Ydf98HSJhIl17ccyld3HQUYMJ1V4HOoVCumaT6YBMPT4MfsrCGXyw+lMA/jLmIfrlH/qLv/ufIgPyX+fXfH9CqOhuBygKqqqAZWPZNrFQ9Bc3SHXlcegCYSnx9Iaqgm3GL3ARCpZqNwrygURQX9fzXvcaVVPi89XMeCCjqgq2ZQFK4jFFKFRFwximia4JUjUnVsxG6GrSvrBMe4891vVvLrREz3Z7DHL2p4D859hT0L7rNbsuHiE+l7u25AAAIABJREFUjF0VKihgxSxUoSJ0iIXjF3yKUBAamJqGjYKi2Ci2TVVNiFjMQBMqTlXbp9a2bY/HKvyygBxq67u6i2+hoKgK1bEwMdNCFyppThe2YQMKthJ/jW3Y2BbxfUx8FLotiL/nV9Sd7fk8kWWTAXl9LVGuptpZoSpoqNimHW/z3TrxsxHUSAzLAjMaD6jRLVRTpToWiZ+LmiBVc6AIBStmJ64L2uqmpwzIDyz7zJD1srIyVq9ezfz58wE499xzueeee/D7/fh8jZcVa24R02ZdACpj0MWrkKrZWJZNyfJSUjqn4EzViQYiLN24lXDhFvI65KDrDiK1J7FtRLCW3E8aIY7LdxHJyQJqCEc9hCLJJ3oXl4M0oVJlWhyV4sKhKDgVhe2RXckduqWbLC70oAmLdys38Pf/HUR5SGFLdR7PnPUj5Yekk1lahrLpce4yu/OB1+aj6vVsUhz0/c158ca/oZpKaqIGi7/dzAcrtxI1LY7pls2E0/rQZ+BFvCT8HLy9kIw1q1g1/WIqjjyVI/9vMpn9fkdU1SmrMSmu7ehP1SwcIpsh3U/ivQ1f8I91y1o0IJfajmlamIHwT7/wF0hqBOufJmaD360mXmYlP079Eb0N31/vMQ2BJgTYUFOb/yEnw9O4YdzjXGU5LHh/F5/z6Ipn0DVpcp5g0nFQp3bEVOJiq+4FdcdrjLr/JAgUhKrHp0XtQ8H4/ii+T2tP6to6QyAQIn7zPRhJ3neN6p0GP7dU3SlJ+7um2lnThGjdo1EgGmvinSTO3ZwcLzUlRvz8rWvzY41fJ0ktbZ8JyHfs2EGHDh0SjZ4QgtzcXHbs2NGiAXnUstkRgsJw/K529xQFr7CxbSj5uoSIP0Le8XnYQDhm8I9/b8a2DAYdfwQ1ERPbiGCv/xz3qjfpoFRxyu+OxVYMlA5O7FgN1ZYPI9Y4i+JdB+dhY+NU48O88506G2t2DWtXFDjIqVAVEhzTweQNI8o1R6h087h5dW1nzjm4kNRsN6lRBd+GVRzrNxloZvHWR1/ydCyXU7t15axDD8KTJqiI+FFrVrGiyOa1VVATNTmuWzbpbo1Pvi9i+ttfM3bAIWyJVnHsCWeyuedhRFb9B/fG73j3tmGk5h7EoacNRU/Lo7p8J8U/LMe/eTXOyvg6qhfoToIbynj7u3V0638S+d0PJT27I3pt8qO2ZpkGW1ctY+uqfxEo3YHDk0qHHv3oesQppPjk3HdJkiRJkiRJklrGPjNkfdWqVUyZMoXFixcnHhs8eDAPPPAAhx9++C/+u7Ydz2xqWjax2n81UZtAzMIfMimvHZOd6xUcnKGjC5VgSQ1F/yth48dbSO2WQvQIN6vWb2DR518Q9heRn2NwTIoHV/F3dIpuoX9+OjnpHgCiqgN/z74ccdQ6lGpYU9ib8urgT5bzreJKFpRUMu2QfDq7dCKWzUuFZZzVK8KoPhbXfWxTUpbCyMzO6IrK95WCHysjTD56O4f6whRXqmR5TRQFasotvtzi4cOSHHrnGYw8tJg0Z/z24qqSTL4qPoYSsyvVpoOamhKWb1pNL0+Q36YX0TfdyXY9g9f8fk446DjyKv0EfvgXKcGtdMp0Y9kKFZaHUttBhzQH+akpFIYVNhSW4N+0mnC4Xm+AcIDDg+r0ILw+9PQcnKnZuFQQsRqi1X4igQosRWBqbsKeHMpdHSh3dSCa2hFXZkdyM1LJyvDi8bhxOXXcmkp2ikbHVCcdUhy4dQWPruIQKrZtxdfmrQlQVbKDwh9W8uP/Pmftv/5OTaUfAFdqOtGaIFZtgqS8HofT87gB9Dx+ANkH9cCbkYXLm4orJe0XH3OSJEmSJEmSJEmwDwXkZWVlnH322SxbtgwhBKZpcsIJJ7BkyZKf3UNeXh5sNAdnc9Bmc7DxV6Aq4NUgw6GQ4VDQVbAsm2ggxtezvgUbMntl8Fjhf8n++HYyoqVJ77/45MPISYsH4TV6OsG8IwloGeT6tnFIr7VgQcDfh6KID9P86fEwxeEIE//9P4JG8muv7t2Z64+vRNeCzP7G4vHl9Z60VNyRTB45Jo1DMgzWhrPwxIIck7YFt2PX3wn7YwS2R9G9KimdnQhHvFe+Yn2IUPGusTsBU7DTcHGIs+kbCIVKFgKbDnY8uA0obkqVDA6yihC1wzkfjZ3O+mqBCJUhYkFELIQwatAjlThCfhzhcmxFxdRcxJxpGI40FNtCxIK4QiU4g8UodvONH0rN7kiXfifS9YiTyD24D5rDjWUalG3bQPGPKylct5zCNV8Ti4QS71FUlUsfepduR50CQFZWCmVluxLaZWWlNEvZmjpeW0rDbWhrsjx71pzlaY/Ha3v7vlvCgbCN0D6PVdi747W97qv2Wi6QZav7nObyc4/X9vq9y3LtvdYuW3Mer9Le22cCcoBLL72UCy+8MJHU7Y033uCll1761X/Xtu14khW77vd6z+3uPYaZeB/UJmIxLKJGDDMWw7IsdF1gGzFsy8SydgWRmtBxOEwsIxbvtd0L8aTTau2n2tTtPlXVsGwlnvCKXfPDbcyk7VEUUBQVoWooQo8/YNfbUCUecKIILDu+dJQNYMZQMVFsEJqKogoQGqoi4klqjGj8+7BtFKU2eZZtYde+31JUEBoKCqZl7yph3dJpSvyfCigoqCigKii2jY2NZcW3V6l7EQqapqMKEX+HqiQtw6YooCrxx1SF2jLV7i3LwjJNLNNI7L89LhOpxMukqCqq0OL/V1suY7wkSZIkSZIkSQeGfSog37BhA7feeitVVVWkpaUxY8YMunfv3tbFkiRJkiRJkiRJkqS9tk8F5JIkSZIkSZIkSZK0v5DjbiVJkiRJkiRJkiSpDciAXJIkSZIkSZIkSZLagAzIJUmSJEmSJEmSJKkNyIBckiRJkiRJkiRJktqADMglSZIkSZIkSZIkqQ3IgFySJEmSJEmSJEmS2oAMyCVJkiRJkiRJkiSpDWhtXYDWVFYWwLJaZtn1zEwP5eU1LfK35ee3789v+Nk5OanN8ndb8nhtqK33X0OyPHvWnOVpj8dre/u+W8KBsI3QPo9V2Lvjtb3uq/ZaLpBlg7Y5Xtvr9y7Ltfdau2zNebxKe0/2kDcTTRPy8w/Qz2/rbW8O7W0bZHn2rL2Vp7nt79sHB8Y2wv6xne11G9pruUCWra20122T5dp77blsUvNrtYB8xowZnH766fTu3Zt169Y1+Zo5c+bwm9/8hqFDhzJ06FDuuuuuxHOhUIhJkyZx5plnMmjQIJYuXdpaRZckSZIkSZIkSZKkZtdqQ9YHDhzIZZddxujRo/f4umHDhjFlypRGjz/33HOkpKTw97//nU2bNjF69GiWLFmC1+ttqSJLkiRJkiRJkiRJUotptR7yY489lo4dO/7i97///vtcdNFFAHTr1o2+ffvy6aefNlfxWp0QKjh1YrpGRNMwdS3+mCRJknRAE0LF1DUiQmDqGrpDJP0u24q2kdgvmkZM18Cpy30hSfuxhnUxTl3Ww1KLaHdJ3RYvXsznn39OTk4OEydO5KijjgKgsLCQTp06JV7XsWNHioqK9upvZ2WlNGtZG/q5CREs22ZLeQ0l5WHuWLyWwsoI+elOZl9wOD1zU1AVpUU/v6UcyJ/fEp/d0sdrQ229/xqS5dmz9lae5j5e29v2tYSmttGybdaXBLnhjRUUVkYY0NPHhJO7MnnB6kRb8ciFh9Mj55e3Fa2tPe7LvT1es7JT+KE4yKQ3VyT2w93n9CYnVadLdtvti/b43daRZWs+e3O8ttdt29fK1bAurjvnH/nHRsqC0Vaph9vrdyY1v3YVkI8cOZKrr74aXdf54osvuOaaa3jvvffIzMxslr/fklmrc3JSKSmpbvI5IVSiqoph2Whq/MTd6g/z5yXrKayMAFBYGWHSm98xf1R/RMxo1s9vDQfy5zf87PaYtfqntPX+aygnJ5XiwhLsSAA1Jbuti9Muv5/mKk97PF7b2/fdEna3jaauccMb3yXahvP65SWCcYi3FTe88cvbitbWHo9V2LvjNScnleLKMJPe/C5pP9yxeC23ndUDl6q2yb5oz+eJLFvbHK/t9XvfF8vVsC6uO+dvHngIBQtWc8Mb3/HcqP6ogMOyME2r1crWEmTw37ba1XiLnJwcdF0H4KSTTqJjx4788MMPAOTn57N9+/bEa3fs2EFeXl6blPPnEELFqQpcQqMkajHm5RWcM/e/jHl5BRHLxu0QiZO8TmFlBKOVAjBJau9Ci6cTeGoEdizc1kWRpFZjWHZS25Dm1ppsK0zZVLQq06bJ/eB2CNluS9J+qGFdDPFzPs2tJX4urIow5uUVlEQtdIfMii79cu2qh3znzp106NABgDVr1rB9+3YOPvhgAAYNGsRrr71Gv3792LRpEytXruShhx5qy+LulhAqdtBk6dxV9L6oJwWf/Zh0h22LP4TXKXhsxOG4HYKqkMG8r7ZSFozGe9DNNt4ASWoHjPVfAGD++CVa79PbuDSS1Do0VSE/3ZloMyzLbrKtUBUlPr+xmXtlpMZsy0ZV4vsly+tg7IkHkebWCEVNVAXZbkvSfkhTFQb09HFevzzS3BpVIYO3VxZRFYqPhslPd1IVMuIjXBesZv7I/jhknSz9Qq0WkE+fPp0lS5ZQWlrKmDFjyMjIYPHixVx55ZVcf/319OvXj4cffpjvvvsOVVXRdZ2ZM2eSk5MDwLhx47j11ls588wzUVWVu+++m5SU1p1j+3NptsLSuasI+sMIr97oDttHa0sYeUwnbn9nbdK8lGyvHh/20kbllqT2Ila+I/Gz8eMyGZBLBwyHZTFreB8KFqwmy+tAEyp3vrcuqa1w6QpGTQyvU8j2ohWEA1FiNTEePP8waqJWUu6Xh4f3waXYxNq6kJIkNSuXYjPhpK5MXrgrf8eD5/fhb99sJz/dybTBvZjzz01AvLMtWBXFk6rLOln6RVotIJ86dSpTp05t9PgzzzyT+HnGjBm7fb/H4+HRRx9tkbI1OwuC/vgwWzMYS+rtADijd06jOYF3LF7L/FH9MfeBOYGS1NKiOzckfraqituwJJLUukzTItehMn9Ufyxg3MsrGrUV95zbm2BlmPQ8uexna7AMi5qyMKF0PRGMQ3x/TF6wOj6fv43LKElS8wrbSiIYh/j5ftPC1Tw36gjO6B1kzj83sbIwPsc7P91JrDoKXr0tiyztw9rVHPL9hgpenwuATe9vZsagXuSnO4H4SdvF55bzxyVpD8zKEgBUXxesQGkbl0aSWpdpWoiYgbWbOYw5XgdFXxTKFryVqJpK0ReF5Hgdsu2WpAPE7uaQ27ZNboqDsmAUiF/XzxjUS9bJ0q/SruaQ7y8MxebkCX35fO4q/JuqcC3ZwvyR/TEVELWrIzTsNc9Pd8p5aJJUy6gqBhRUXxeMzV+3dXEkqU00nE8O8bbCrIjQ/5zu2AKQ0xVbnNOj0/+c7lRURGTbLUkHiN3Vv5oCnd0680f2J1gVJVYdpWjJFvqdczCGIm/OSb+MvJfTAkzTQkvVOP2GIznzxqM55MR81v5tHa5ADIdlJeYI1u81nzW8Dw5LXllJEoBRWYLiSUfxZkEsBNFgWxdJklpdk23F+X1wGzYrF/+IUW0ghGzGW5IQKpVFQVYu/hG3YTPrfNl2S9KBYHf175pX1/HWH79k7d/WkevVyc/zcvSIniheIRO6Sb+Y7CFvIYoJnzzybWIuOUDFtiADCo4kYpmJOYJ1a5O3xBqGkrSvMquKUVOyUNxpAFjVpahZcr6sdGCpP5/ctKGmqIYNL63Fv6kK2NWmyM7ZlqPZCkufWknQH2b7ijJ83dJ4dEgPPHkehNIy6w9LktT26te/hmWjqwprXl1H4YoyALavKEu6rpekX0PeWm8p9RK71Qn6w4nhhXVzBJ2miYgZskGXpHqMqhJUrw/FFQ/I7aCcRy4dmOrailTD5j9zlieCcUhuU6QW0qAt92+q4j9zlpNq2LLtlqT9XP1rdXfMTgTjdWQdLDUXGZC3lHqJ3ep4fa7ENy6EiqlrRITA1DU57FCS6jGr/SjudBRnvFfcDlX9xDskaf8lhEpIVzj+lmM4+g/98HWL36iq36ZILaRBW+7rlsZxE4+gWlNk2y1JB4C66/VqTeG4iUck6l+QdbDUfOSQ9RZSP7Fb0B/G63Nx8oS+GIqNECrFUYuCBasSaxvOGt6HXIcq77ZLBzzbtjFDVWgONzg88cdCFW1cKklqG021FzMu7IVLJhFqFYZic+rV/fj0qZU40xx0GtGD699fK9tuSToANFX/zhzRG15fT6Qqmriul6RfSwbkLcQ0LYRXMKDgyPhwFjXesJumhalriZMb4ssoFNStZSobdelAZ0TBjIHuRtHdANjh6jYulCS1jaiqNmovpnywjudH9UeR85dbnGlaZOenMKDgSEJC4YoG68LLtluS9l9N1b+3vL+W56/qj9u0E9f1kvRryYC8BZmmtSvZTr3zdXdrGxqWjWi10klS+2RHAgAougtFc4DQ5ZB16YC1u/YiZtmo8kKwVSiqQsQyiSlCtt2SdADZY/0rE7lJzUjOfGgDdWsb1pdYy1SSDnB1ATl6/BxRdLfsIZcOWLK9aD/kvpCkA4s856XWIgPyNtDU2oZP/F9fAJnkTTrgJXrItdpG0OGRAbm037Fs+2cl9mxyLVy59nWb2N2+cCk/b19KktQ6mitxsqx/pdYih6y3gYZryzqFQllNjGv+JpO8SZIdru0h1+KZjRUZkEv7GSFU1pcEueGN7xJ1/uzhfejgFsSiycMgG66Fq6mKXPu6jTS1L1JUhe01MSYtWC3bb0lqB/aUOBlAs5VEbifb2nNCNln/Sq1F3sZtIU6nwCEUHCg4hILTmTzDzDQtHJaFKxCjfHsw0ZjDrkQxUVXuHukAFIkH33UJ3WRALu1vYkJNBOMQr/MnLVhN2FSa7Mmpay/SbPDEbDS76ddJLUcIFbfQECa4TAO3iOKojrFDtt+S1K7EE7E1PifDKthBk6WzvuXdO79i6axvqSgMNKpLhVBxqgInAqcav3avW4tcxAwZjEstQvaQtwCnUxCpiPLl06sTS5799qo+ODMcRCK7ej80W2Hp3FX0GdtHJoqRpFoNe8hxeLAqd7RdgSSpGekOlUrDaLLOD1ZF8aTqNEwVJIQav5BssIym8Ap5cdgKbMuGoMnHc5cnvv/fTezPP+eukO23JLUzu0vEFomZ/Ke2DgUI+sN8+tRKBhQcmahzZV0rtRV5C7cF2IaVCMYhftJ/+fRqbMNCd9T7yq34c2YwJpNGSFKtXUndaoes6y7saLANSyRJzSdgBvixZHOTdX6sOoptWURETVJbodkKnze4kPx87qr40EupxYUDUT5r8P3HAoZsvyWpHdpdIja3pifO4TpBfzhpFSRZ10ptpdUC8hkzZnD66afTu3dv1q1b1+RrHn/8cc455xyGDBnC8OHD+eyzzxLP3XrrrZx66qkMHTqUoUOH8uSTT7ZW0feeSZMnfXGFny1V23ZdaKng9bnY9P5mZgzqJZNGSBLxgFzRnKDWDuDR3RALgy2XGJH2fYZl8ND7j/PgsN5Jdf6MQb0o+qKQlYXrGP7YxOS2wmq6TUE2Ea3CMqxG33+4Oibbb0lqh5pKxDbt9wexo2QzXp8r6bVenys5EpJ1rdRGWm3I+sCBA7nssssYPXr0bl/Tv39/xo4di9vt5vvvv+eSSy7h888/x+WKn0BXXXUVl1xySWsV+ZcT8ZO8/knt9bnYWV3K5Lfu5+0bngTdQ5UNJ0w+im3/2EqqZfPMRf1AVdBVBd005fAY6YBkRwKoLi8QT7ai1PaUEw2BM6XtCiZJzUBTNYqry5j3j6d5buS1GJaCqoBZFSV7cD4T3riDbf4ixs//U6KtqLbhuIlHsOGdjfg3VQH1LiRlM9HiVE1NtOm+bml0+31X1DwPx990NKFAFCVs8PjZvXBkONGETPokSW2pfiK2mG2xfudG7ll0D4oNsy+9na1Ly8g7KR8t1UFKmiMeCdWNcFcbX7/n988ipCvELCGTukktptUC8mOPPfYnX3PKKackfu7duze2bVNRUUFeXl5LFq3ZKZrKb6/qkzSHvO+lXZi0+F5yU7MoD+lMXrCCwsoIA3r6mHBSV65ZWC9D6/l9yPMITNkhKB2A7HAA1ZUCdnJAbkeCKDIgl/ZxKSKFZ8fcw1vfLKUyYjJ54ZpE3f/w+YfRK+9gvt7yXaO2Ij/dycwRveH19USqopw8oS+GsucMwVLzcKU4OGVCX1Ys3ojvrC489c12Lk7txLT31iVlyc/WFSIRo1EOAEmSWpdpWgjTIqJUMfqpaxKPv7txKWcPG5ZI+lb/mjsWNTEUm5Mn9E0MW8/vn0Xe0O5c8fIKuYqC1KLa7RzyRYsW0aVLl6RgfP78+QwZMoRrrrmGDRs2tGHp9iwSMXFmODhtcn+Ou6kHWSNcTFp8L19v+Y4bf38tkxesSSScOK9fHpMXNsgGuXA1ETlfRTpA2ZEAqtO764G6HvJYTdsUSJKaUSxq0SWtM1f97vJEMA7xun/ywjVcd8Z4gEZtRWFlhFveX8tRVx3OgIIjUWSSoVajqAp4BYeN7M2UD9ZxXr+8RDAOu7Lkh+TukKR2RaganX274ojzjj6ncQb2etfcpmmheAUDCo7k3LtO5LCRvShoeI0uV1GQWkC7zLL+73//m0ceeYR58+YlHisoKCAnJwdVVVm0aBHjx4/no48+Qoifn8c0K6tle9dyclKTfjcMg1AwxOS37mebv4iz+55Cj6xO3HVmmKqIxfP/3UmaW2s6Q6sN+Vkp8QuBX/j5re1A/vyW+OyWPl4bauv9VydihlCcHjIy4suehQPp+IFUp4V7P9vHv0Z7K09zH6/tbfua2/byUJN1v1AddPbl0btDN248tZI0p5poL1buCGApCmlZ7jYq9S/THvfl3h6vPp+XwsowhZWRpHa7X8cUrji2A2lOlVgsQsfMVITWevnV2+N3W0eWrfnszfHaXrettctlWRalFTavXvkYG0u38N7KT9A1126vuTs1Ub5A7Tnf8PW2orTK9rTXfSk1v3YXkP/vf//j5ptv5oknnqB79+6Jxzt06JD4ediwYdx3330UFRXRqVOnn/23y8oCWFbLDPHLyUmlpKTxWsld0jqz4Lo5KCiUV0QZcfvjbCsup3NuJg9NuhTLtMlPdyad8PnpTmqKavAHYj+7F2R3n99aDuTPb/jZzVWBtuTx2lBb77/6YoFKXB2zqagIAWBG4jelKkpKCKS0j33c1pqzPO3xeG1v33dL0B16k3W/UxO8ff2TlPormfro/KT24vXVDhTb3qe+m/Z4rMLeHa9126DqGvnpTqpCBvnpTrI8On84PosbZ+/aT89NGUOnrAxisZbvLm/P54ksW9scr+31e2/tcgmhUFhRyfj7k89NsyLaZL2rKTRZvrpzvuHrW6Mebu3vTAb/batdjblYsWIFBQUFPProoxx++OFJz+3cuTPx82effYaqqklBenshhIpTFTgROFWBZYLT9KCYOuPvf55txeUAbCsu58bZL4EZ4eHz+zTKtrvhnY1yqQXpgBQfsu5J/K7otb2BdcuhSdI+pmG7IIRKqqow8/fJmdZn/r43XkXBiqmJC0nY1V5MPrWTzN7dhuqyN7+9sohpg3tx5Ql53Dj7paT9NG7GfEKxWBuXVJIObGEz1qgOHTdjPpFQqNGqCLOH98GrKAjROCRqKmO7XEVBagmt1kM+ffp0lixZQmlpKWPGjCEjI4PFixdz5ZVXcv3119OvXz/uuusuwuEwd9xxR+J9M2fOpHfv3kyZMoWysjIURSElJYUnn3wSTWtfHfxCqNhBk6W1ySC8PhcnT+iL8ApCUStRMdTZVlxOhxQHGV6N50f1p8ofwQzG2PTGhkQmXZlBVzqQ2LYdX/bMUW9Ibl1St7AMyKV9z+7aBTtVsP319cz6fXeEV4/X/a+vp+uYPhhm0+0FMrtvm+uAwrWHd8ShCch0NrmfDNNE24vpdJIkNa/d1aG2sCl8YwNzhvXE5XMRKgmx4cW1fF+bKFM0GJVaP2O7Ydkyy7rUYlotop06dSpTp05t9PgzzzyT+PnNN9/c7fuff/75lihWs9JsJXHRBfG1Cz+fu4oBBUeiCZXOuZlJFUTn3Ex0oRANG7hVwVfzVjdaKk0uayMdUGJhsExUhzuRqVjR6rKsy4Bc2vfsrl04Y/LRRKqifPPkysRr6+p8jabbC62JHhyp9Wi2wtLHlif25ZHje+1mP8lgXJLa0u6uuc0aC/+mKpw1Jl+/+G3SNXfd9XrDVRLqMrYLABO5ioLUItpXF/O+ziLp5Ib474oFqcLJc7eOYVy9+SzP3joGl9ABhRpV4ZiCIwkVh9jwzka5rI10QKoLuhNrjwNoDlAU7EiwjUolSb/CbtoFbJtTr+tPaXkEnAIiJtmZTgzFxiV0nr11TNL8x2dvHUOKw4lqKtimjSIUTNUmGpOXh63GAmeag94X9UR4dTTTYu6ky5gw+8WkeapuXW+VOeSSJDWtqTr0uSljKP57afz5VD1RL/u6pdHt910RXp2QUNCVppc0E0JFRwXTjidcViFqy95yqXnIgLw5qfEejoa93FU7a/jnEyvI75/FwunXYVgWuiZQHU4iNlSGYhQsWLVrPdPL+pDv0ojJYTHSAaZuWLrq2BWQK4oCmgtkQC7ti3bTLqhOlZ0xg4Kl63etQz68Dzm6hhoz6ezLYOG91yWGP6c6nUQqYnz+dL2h71f1xZGmyaC8lSi6QqcRPSh4f21inz0x4nDe/PNETMPEIVRSdCdROb9UklqFEGo815IFqGAoNqZpYZo2+RnpLLj3WgzTQmgCoTvJHJlF31HgtuP1cE7vDDIHdqZg0Zo9rjMuhApBk0/mLk/UvydedhjONB3kmuRSM5Dj35qRodicPKFvfNghJE7YLf8r5pSr+tFvcDccIZWdX5bG9VF0AAAgAElEQVTij6lc/pflrCkKNFoTcdKC1QQh6QTXHQLDoRPWNAyHju6QQ+Kk/U+ih9yRvKyTorux5JB1aR/UVLtw8oS+BFEa1f2TF6xmzc4gxTELxYC1b24mWGIRDCoEDBAOlaMv7ElWt7T40PenVyEsmfiztQSBW2qDcYjvs2te/461ZTEmLPiBckPw/SdbsYNmkwmiJElqPon8HLO+5d07v2LprG+xg2b8+tipE1YFqsNNDQ7G/20tZz/xH8a9vJyN5WGKYyanTTqCzoO7Mbk2GIfdrzOu2QorF2/k6At7MrDgKI6+sCfff7KFYGlYJl+WmoXsIW9GpmkhvIKBBUcR9IdxpznY+PVOug3qBrZNxCFQsOk0sAtF1RFuHngI2SmOptdEtGwE8aUbIpZBoCbKlooIzywroqwmFs/MnqIRDRttsq2S1BJ2BeSe5Cd0F0Rr2qBEkvTr1LULAwqOTOrFiZhWk3V/pwwXj/5jI3888xC6D+9CSXWE8kCAd/9bztW/7UrRV4UccV53lr/9I2WbqrBNOa2ptZg2Te6znjlunhjeC9OK0PHUDhStLOeg/jlyrqkktaCm8nOU7wzi8qYRi4bwByOkexw89UUhhZUR+uWnMvbEg9BEPICOOAVllZE9XoPXURWF3qcdxLK/fJ/oIT/hkkPRPULmeZKahQzIm5lpWiBUvnphDcdfeijOI7N5ZUUhZ/fpwDMfbeTiYzsx7b11iaExT17Ur+k1EVUFXVWpiUWJxAwM0yJDN5k+qCvlNTGKK6rJ8vjQhBwqI+0/6gJy1emCeveaFN0p55BL+yzTtHYFZ7XVtaqJRnX/gJ4+7FiEG07qSDgU5O75b/Phsu8S65A/9eVmJg/qRmVVgENHdSPsj6DqCtSusiWEQtiMYZgWmlBxCR1TBuzNRlWVRvvs8uPzsWNhLNPEqWmYhkFqn1SEpkC0DQsrSfu7evk5fN1SOXREN1SPim1EqKkJUVLi5/3vNzLxrN8y/rgcPE4Hj36+naU/+BNThMIxa7fX4A3vqNUF4xD/3GV/+Z7TrjtCJl+WmoUMyFvICZccSo2mULBoDY/9X1+u+9sqbh54CNPeW0eW18HNAw8hza3hr4ny4Pl9uGnh6qT5Ky4sSquDBMMRyqoCZKengA0j73gyKclPhywfyIBc2l+Eq4HatcfrD/7Q3diyh1zaxwmhYIgotuLEsm0eHt6HybXD1gf09DH2qExG3/lEoo5/8LqLKCmv5pt1W7hx9ku8ce9EKqoDjH+sXqKiW8fQMSMdgMKKSma99iEjBhxHVnoKuZmppLvcMsFYMxBCwYJG++ysg9383592tctP33I54ZhBNMXA5/XIGyKS1FLUunng6eQP7EBJZTVlOwO8vvQ/jDnnFP77/UaGnHwUo6c9lTg/5065gnS3xqIVxUxesJp7h/Rm2uBeSR1ldeuM18XjQqhgw4mXH0Y0aLB6yWbKNlUR9IdRhUJMJl+WmoEMyFuCAqqm4PS5KKyMIBSFwsoI2SkOju+azlUndcWwbEzLxl8TI80peGZUf2wbVAVcIkooYuIPBDEMk78u+RdTLhnMZfc8m1jCYVtxOePvn88b916HEzmfXNo/JHrIHW4I7UqCpegu7Bp/WxVLkn6VukA8gpOo6cKybMJGjDSXztyR/REq2LEIF97+WFIdf9NjrzFt7FDG3/8824rLiRpGImtw3Wsefu1D7h5/PoZpUl0T4roLzuAPD76YdOM2PyNdBoa/gmVZ+GN+vCIXj0Pw3KgjUBSwYhEuuG1O0v64auYLzLp+JMUVVaR5XSjI+aWS1NwcLo2gpXD8zUcRrKrmxx0leJwOHLrGdRecwWNvftTkdfOEGc/z8t3XsqE0xMrCanweB7OX/sjNAw8h06uT5dbx6SqhSHzYUd089Y9rk7nl98+i/5g+hEIGZjCG4lDBNHGqolFiOUnaGzIgb2ZCqEQDMVZ/tIXuI3qSn+7EtG0G9PSR5dW59PiDKKyMcMfiXVla7x1yKJ0znGgKmDWVKEYY3TTokaFTXBVj4gUDqQyEktZThHjlEh8iLwNyaf9ghwOgu1DU5GNa0V1Ysodc2gcJoeCP+amsdjF18Q8UVkZ4bexR2LbC+JdXJNqBx4f1YFtxOReffizXDzkOXbGxFBWXO4XF0y4hEDXRVSWpHTi6VxfGnHMKF9z+eCIAf3jiSHIzUtlWXJ64cbvg3mvRZXP/i5XXVFMd9TDxzeWJ/fXgsEPJUwO8PXUkQmjEbJWHFnzBK5/8l9zMNP784rtMGzcMl6K3dfElab8hhIrqFGyvinHjwtVMO+tgzFAVt819k23F5Zx9Qh8eGHcuD1w+ENMMsuD2i5n11r955ZP/AvHr5vJghLEnHsQDH29gR1WE8/rlkebWyHHreGMW0Xo93vXnqfu6peE7qwtjX1+Z1JveAYWlj+3Kvn7yhL4Ir5BBubRXZAvdzOpO3t4X9eThpT8ybXAv3lq+g8kDD8EfjFERivHnJevJ8ujceGpn0pwq1YEgSpqDqH8L1aWFvDx1DP7Czfjyu3LJfS/h8mTgTMvgw3suo7gyxKy3lvHND1vonJuJQ2ZylfYjdiSA4kwBGvTmaS7siAzIpX1PmBBby6uY+cmOxDxFlyb4w2vxi7p+HVO44tgOWLbFhw9NIjVczLyrz0i0AWMefoPvFjzJD8s+ZuzshSyePpY/vfAR3/ywhWuGn85Nj72W1AM0ec6riV71uscM00KX921/sYghuO2d7xPtdsdUndTKjTw2aVhiP42aPp8bzzmCPt07srmojBEDjsOybOQANklqHkKoFEctUlWVGxeuJsujk5+mURQTTBs7lHVbtjPyxJ4ESrYyf/KFiXNz4sNvAvDKJ/+lc24m5SGTrAw30wb3Ys4/N7GysJoBPX1MObMHQVVBU8BBbU6oevPUu/2+KwUfrGuUkf3RAT2S5pZ/PncVAwqOlEkdpb0iA/LmVnvyOtKdLP3BT2kwxtgTD0JXIc2tETEtsjw6fzg+ixtn75oHuOiOy/Bv28Dr06/BX7gZgNTsPCLBarIycyAS4MP7J1BdWsS9019m9kcpFFw0CKfQ5FBEab8RD8i9YCcf04ruAssAMwrC0Ualk6S9F7MMMr2ZFFbGp1z0y09FUUgE4/XbgmWz/8Czky8gNTuP82+ZhSfdR8BfzFlX3sayhfOYN+l8Rkx9gvtGHs8fX4Ws9JQmR05lpOxapaBzbiaavHH7q1i2mtRu33fZGbxxz8ik/RQNBamp9DP0qG7ct+Bf/N8ZJ+LQtEb3FiVJ+mWiqkrBglU8fXH/xPk4YuoTiZ7x+0efhmVEWTJ3euK8rKn0s2TuPVxf8CCfrdrAQ5Mu5fXV5dx8ho8HPtqQCMYnnNSVsX9dntTzneuIzx33+lwE/WGEV28yI7vw6hz9h34Ir44ZjLHp/c0yyZu012RA3txqk0y4PRr56U5WFlZTsGA17119LKoZJUMzmHZWVy6Z9iS5GalMGzuUjBQPlhHF4fYmgvGu/U/g3In38sqd4xJ3+S6+6znenXM7C6aOYsbzn6N6fRD0gxmlqjSI4kgnbNpoqhJPSFE7XEYIBSKVmGYUIRzglPMJpfbJDgdQGy55BqDH1yW3I0EUjwzIpX2HrmqkufREJt+xJx6EZdnkpzu54tgOiWD86F5dcKjxG7EN6/6xsxfQtf8JbF6xDIfby19vu5j58z8FTwav3XYxKQ6BPxBh1lvLKK6spiYST+9dN4dcZlv/dVy64MoT8rhx1jy2FZfjS3E2uZ/GPPwGAJOGHIcubDQziHCmE5VJ9STpVzNqlx10CiVxHV13Q3LCWUdTXVZERm4+p46a2Oja2etyMv36Mby+upyLj+3Ea19v54YB3bn8xIPokOJk3MvLG/V8PzeqPypw2qQjCBSFEBmOpjOyezWufXdNIpifOaI3Sr3VLyTp55C3zZuZLWDg5KMIY3P3Ob3JT3cyrH8u5eXljLj9MU675j4CwSC5GanccslgXl/6HyzbRnc4iYaC+PK7AnDG2CmJCgXAX7iZV+4cxxljp+Av3IxlRAkWrmPOFScx/ffdmTX6REo2rebWt79nzMsrKI5aCKHi0FWs4E4qijaxY+23LLjvWmqK18eDdElqZ+I95I0DckV3xp+X88ilfYgQKqmk4HE4mTa4F/npTtLcGjUxkwfPP4xumY5EMH7LJYOJWjDo6jsa1f3zJg3njLFT8OV3pabSj79wM5U7txLasZ7Sz15j3qVH8+k9I7lv5PH89U9X0vfgfL586jYW3HutTOjWDHxeB10ynImLf38g0uR+mj/5Qmzbxg4HuHfwIcy57LdU7vgBhy4vtSRpbwihEMMgZEaJYSCEgkNTWDjuaKLhGjTF5sU/jWfkwOM4ulcXOmelEvAXY9s0ee0MFp1z0jivXx6v/Hc7Z/fpgFtT6ZTqxLbtJnu+C6sijHl5BX4bjDQdS1V48Pw+5KfHr0fy053MuqAPDy/9MSmYv+X9tchFWqW9JXvIm5EQKka1QXEgxvVL1yeWN+udpXHBbY8lLrxSPS4mXXQW8xd/xnUXnEEoEuXWF5bwx/OOZdT0+bw8dQyedF+iQoF4j/kZY6eQd0gfJjyxGFSNeTcMTap0/nbLBUyYuYTr3iuiYMFqXhrdn5rC73lu0vlJdwo/eGIaw//4OGhpTWyD7E2X2o4dCUBGXoMHTTzaejKPT8Xa9CiRfneDcLZNASXpZ6rLzvvJ3OX0vqgnr6zdyc0DDyEnxUFZIIoZqqLGNHjttovplpPOppJK3lxTysgTeyTV/RDvNc/r0Zern/qAiqKt9BswlIC/mIUzC7h67ods+OYzNq9Yxhu3XczEF7/ENFXctVM7ZP3966mKgksXdM7NZFtxObPeWsaz1w3GX7g50TbXDY81YhHcqemJEQ3zJw3juhe+BJHa1pshSe2aECqaraAKhU1+f2JFibrlHbN8GQSrKxg14/ldy5jdcjnYsLOynK/feoHhfzw6abj6R/NmsHnFMgzDwulS6JufQs/cQxCqgqpCuDqGoqlN9nznpDi4a3AvysMGt7/7fWKpwycv6kdV2KAyFCPNpbH0h+QVYAorIxiWLdNHSHtF3rZtRpqt8PncVeAUFFZGWFlYzbyvthKNmYk769cMP50/v/guXfOyGDHgOMqrg0ye8yol5QFKTA/pnXtx3bx/4Mvvmugtrxu+vnBmAfcOOZTXp19DuLKU1OzkwMVfuJlMR/ziq7AyAqHyRDBe9/wrd47j+KGXY5nJY2mEUBBmFWb1DorWL+elKRcz54qTZG+61KrsSADF4a73gI274l3cfE8saCJqvsex/W9tV0BJ+pnq2oOgP4z/2xKuPrkrD3y8gdvfWUuuG55a8HdSwsV89KcLmXleLz7604UM6J5GedhK1P0Qr/+HTLqPJ686kz+fdxivTruSsyZMZdU/38VfuJlgeSlnjJ0CxOt4MxZtq03erzlVjef+OIbOuZl888MW0F30GzA00TbPGXMaC2cWEA3XYEQjSfvEMuQ+kaQ9qbuB+c3rP1AaDict75ibkcrO8ioi4RAlFdXkZsRvbm0rLmfCzBfwpXlx6A7Ovf7PBPwlSefjuRPvpd+AoWz3V7OltIq1O4Oc+9R/GPfX5Wz2h6nE4vWVO3ioQc/3vUMOZWdVhJwUJ8GIQZY3foNz6Q9+/vDaSkoDUa57/TvCMSvxvjr56U40VV43S3tHBuTNqTahmxmMJU7QsSceREkwRufcTAAyUjx8uOw7CksqyEpPweN0JIavFzz2Gkf/4WHOuesv+E0Xox5cgC+/a5PD1+cXDGfQ1XckfbwvvysRNGYN78Pzlx6BbUQb9bT4CzeT4stFFbuWYhFCoaZ4PXMuP4np5/bi9enXcO7Ee0nNzuO5G4ZBpLIlvzVJAsC2LYgEUerNIXcE/4Mj+A1R1xH4v6vBEJ3Qdv49nuBNktqz2vbA1y2N/LMOIt2t8eyo/jw0/DAM0+SSUw7nb38cmVSvL5g6iuqaKMOnv5wIygddfUdi5Y26182ffCF9f3cuvvyuBPzFpGV3BOJtgKrLHAstwTRtcrKyePWea1nwwE1oms7Qmx5scnqBbVnJ+0ST+0SS9qTuBmbeSfmUBSOJYLxuOs9tc9/kpKvv47a5b3LLJYM5ulcXIB6Uawpke1RUTSSyq8OuTqihNz2IaYFbU3A74v3WdcsPV9QYHNk5g6e/2MzTF/Zl8dXHcc+5vdEF3PneOoY981/+vGQ9E3/XjX75qYn3prnjeaJ2VkcT05GAREI4hyXzRkh7RwbkzUgRCl6fi03vb2bGoF3zBRFOnq29sx4zTDrnZvLAy+/jS/NSE4ky6aKzGi1dc8k9z1Lu7sTpM5fQuc+xnH/LLCbO/wfjapP7+As3k9WlZ+KizZfflYsfXIAzLZsHPt7AFS8t58cKI6mnpe51adl54Ezf9WCkkuduGLbb+eqKAsKogkgpwqiSPeZSy4iGwLZQtHgPuWLW4Kr8CNPTg4jaK/4SJQ8lVokIrm/Lkkr7OCEUYiJMjRIgJsItU6fVJvjs9vuuaA7BtooI419eQSBiYqOSm+5u8oaprpjM/mgNf5j/GVPe+YHMzj1Izc5j3OwFiTYgNTuPFF8uF9/1HP9+6wVcKWn48rtyxexFKB4fESEwdQ0hs6s3q4hp82OFyZ1/30JJRTXhQGVSItZxsxcw+t4XELqDFF9OPNHb7EUoHk/LHWeStD+ovYEpvDoRk0QnVlNLO9702GtcM/x0IP463ayhbOt6woGqJuvKcKASt2YTMmyqQrtu5hdWRnA7BGnu+LDzaE38uaqQwS2Lvk+aFz7tvXWMPfEgIB50h6Imd5/Tm8c/3cScf27i5oGH8PylRzBv9BHkOlS5Brm011plDvmMGTP48MMP2b59O++88w69evVq9BrTNJk+fTqfffYZiqJw1VVXMWLEiJ98rj1RFDjxssP46sU1FL6xgUeH9MCb5iRiWmDpzCgYS/dsDw9PHMnkOa/y5IJPuPK80/C4HIls6xWBGp5Y8AnY0NkVoQYFy4jy77deYOXStxLzwD99eQ47w4Krn/8C1Yqhaw5ibh+X/WV5Yjkdh64xZtYC5hcMT8whHzd7IXpqXlLWV9Nsuifdk+6j34ChBMuLk+ahj3tkEZ7cHnJuotSs7HB1/AdH/E6zHvwaxY4QS/stdjDeMJoxLyggqlZiph7aVkWV9mG6Q2VbdSFj593ONn8RnX15zBt7L/mejs1apxmKzckT+lJu2uhCJRwz4z0vqoqmqzjS0/HldyU1Oy8xB1lVBRkdsrnvkgGYpoWaks22rVsYMum+RC953ZrXadl5LHrgRk4dNRGHJ5Xx8z4nrKcz+qXGS/fIi8PmoakKb68sYtrgXlQEyhChAP0GDOWUi68lI+8gyrb9yNuzplBdWsS42Qu5/i/LEGoK4WCEqYtnUzDocjqkZqOZDtl+SlJ9tTcwzWCMzGwPT08Zw1Uz5pOR4knkX7pm+OmJ6+T87Aw652by0tRxmJFqPnjqbsY+/AaX3PsCgfJSAv5i/v3WCwyZdB/ejGzcgEtL5c73d93MrwusY2Z81QtvmgNbVeiS6eLmgYeQ5taoChnM+2orKwurE73is4b3IcujE4ialAWjFFZGeODjDcwa3genacr6VvpFWiUgHzhwIJdddhmjR4/e7WveeecdtmzZwpIlS6ioqGDYsGH85je/oXPnznt8rj2xDJtvF23g+NGH4slwoqhg1BhoKTprtvtJc6rEDIP7X1qcCMDD0Rgx02DavLcSSSqeuulS3MEinhl3alIytqqyIjavWMYrd47j2mc/plTL4sYlP1IWjDJjUC9SYtFEMH7rERZvXn8aqdl5jJj6BDldeiKcHnA0XoJFCAe+/K5JQbkvvyvRUJBhNz/E4+MHJvWeP3fDMCY+/0WTSeEk6ZeyI4H4D7oLAEfNckxXV0zLgaLHL16tqImVloNa+R10an835aT2TQiFylhFIhgH2OYvYuy821l03ePouJrpc2qTE3l1fA6F4kCMPy9ZT2FlhEVXHoNtGDz21r+Y9Pj71JQX8fLUMaRm5zFk0n3MueKUXctozV6ILy2Nl645L6kOfnnqGK595iOOH3o5n748h8FTHmOnlcLtb6yOtwH5qYw98SBChkXEreOEestgxsuGBajxGwfyAnL3hFAJVUVwInArMOGkrsz9YjNXntiZjHSVs6/+E/MKLmi0POlzk85nwnOfsnNFEM/B6Uwb9kd2VBWxqWw7Hoer2W8ASdK+rO4G5srFG+k5shcPfFHO9OvHkJvl4uwTDmfMOackesrjyzlewaL7JlKyYyux8i2kZeVRXbqTeZOTz8V/vvQIp1x8La9Ou5IxsxeR442HPfnpTu4+pzcuXeG5L7cye3gfUoUCAiotmwc+3pC4sTltcC9e+e92clIcPH1xf1JVm1gkRrpQmT+qP4bVeLlhSdpbrTKe7dhjj6Vjx457fM17773HiBEjUFUVn8/HGWecwQcffPCTz7UrKrjSdBQF/vH4cv71whoUoLi0jCmz5jFk8gP8WFhCcUU14+9/ngunPsGOsgrG3/980nCcYEUJr956UZNDyOt+r4hYVEbiJ/7xXdNJTXPgSomvkTjhqBTevDU+j2bzimXMveYcnrjqTLB3k3HXmc64RxYlDX8fN3shnXofA4posvfcMiJy+J3UrOoCckU4saN+hFGC6e2FbRkoqgBVYEdDWK581JrNP/HXJKmxMCGKq/yJYLzONn8RRjPlJahLTrR01resWLyRsAEFC1Ynhj8+v2wrmqbx2coNbK8MJ3q+zxg7pfFc8Unnk+5uug6uKN7OwpkFnHX1HawoF3Rw2Tw1vAeLx/Vl5uBuHJKhsujbHYz96/LEMpj1y/bunV+xdNa32EFTDm3fjbrva8nMr3n3zq/4eOY3dEnRmXJmD/L0Goo3fZ8IxqHxdK+y8nJyjvdx/dL1DHnmG6Z9UEaqpxsLv/6YMKE23jpJaj9M00LxCo4e0RPbjidPu/GdDdzx4Wb+NOa8RsPWx9//PIFQGKdQ+OCpuxkyeUYiGIfkJMZCd9TWp8O47aQM3h3Xl+dHHkrXVIHPYfPH0w9mx6IfeeuPX1K4NZhUX9cNV580oDu3v7OWq15ZQdhWEmUWMQOnaeKwLDRbwYnAqQpZp0p7rd0se7Zjxw7y8/MTv3fs2JGioqKffK49UTSFoy/oySePfIsrzcGJlx5KgFhStsjZry1JDFnfVlxOVnoKuRmpSUPWs7zsdgg5xAPmHUGbP3+xjj8PORSHpnLN31aR5XVw9zm9SQkXNx1Em7Em97hp2nhyezDx+S+wzFg84ZszHRNQzUiTvefFm74nLadKDl2Xms2uHnI3BOLDyiwtF8z4w4ruxI6GsZ2dUCqXo5hBbOFto9JK+6KYZVAaKKezLy8pKO/sy0NTtcSx9mtotsLSuatwpjnIHNiZnYFI0nI6i1YUc+0pXZl7yxVoobJE3dpwqUuoq7fNJuvgtOyOjJj6BGk5nelUFeWC2x9P9B49eN1FzF/8GZP+7ywgfkNg3ugjsCybmkAYZ5qDoD9M0B/m87mrGFBwZHNs+n6nbl8G/WEgPsc1sCNEwLTISI3gcHt321b78rtSXBnCkxXjqt90ZtoH8R63yQtW88RFl6AoMiCXpPpM08IEdKEnliFbuSOApmqNpnV+s24LRswkK8VFdWkRocry3SYxDviLE78X7ixlwhMv8PDEkWSnp/C3T/7Neb85inBV7UoItask1VdYGaEqbPw/e+cd4FSV/fHPq0kmkymZPgwMitgoVsCCuoiLXcqCCoI0lVVAqoDSFUQ6LAIiVRBQQIodlxXXRVbQ1Z+gIKDSp/eZ1Jf33u+PTAJhhqJi283nn5kkr9y8vHfvPfec8z3szgmm1Z1a0iy80FndV9idVlr2aYxkl6Ie8yjnzO/GIP81SEqK/UWPL+oCXpeGNU7livsuRA8Y+GUjbIwDfLH/CC+seIc1zz1OIGDgdMQwb2g3ArqBKArsOXScq7MbMGjRZmx2B5VlRbw5fyKVRXm4y0vCwj1LfjDIKfeRFKvSZ/UucsqDk77ZHx3khdYptU7gVKuVuOSgSqRhmpS4NPy6gSqJJNgVROHE9TEMg9wDu3lv7lg6j18cVpI9OSSvsiiPQSs/xZkSLL+WkvLb1ln9Lc//S5z7l75fT+W3/v0qjuh4AIczAUp3gByL1Z4IRrBEn1u1IBl+bEn1CBRAolqE5Ew/80HPI7/19TmV31t7zvf9+kt8v4IKjTU732f6g8MZ8trkiBzy9IQkRDHo1TAMg6KqMvwBP6qskhybEP7sbLhKPLhKvFzyQEMGbdzLU60bRNS4bZIRS0V5JT5NIyPJGe6rQ/37qf22pFjp89L7eAuOYLM78LgqEWMSeHPGMG7pNgAz4OPNf30RMWFd+s6/6NSqGY9OXsbK8Y+zcXcBeZU+eqz4isx4C5M7XgzrvqfkUEVQSEkQSUmJOd1X+tn83u5VOLf7NfRbRiCA+2A5CVepSJJU61jt97joPXsDXxSa5JWU06yOM7x7TrmPMo9OusN+Xq757/Hahoi27fzxY/rX3+t3O1O7DMOgqLwKPWCg+zVe6nAR+VUa73xbRmlVVURaZ2jB0SH52TR1CJ3HL8ZdUXv/aU9MZuO0IeHXJVVBBffBc17j+T5/4eE7rqc4L4c/9crEU2HgS1BY1u0KSl1aOHc8M95CUVXQYM+Mt2BVZZJjTzy7ngofHyz4KmLhbtuCr2kz7BpszsiSaOfzmkX57+KcDPLXX3+dDRs2cODAAdxuNzExMTRs2JAOHTpw//33n5eGZGRkkJOTQ9OmTYFIr/iZPvsxFBdXYRi/jDc3JcWBoRu4y3w0vqs+O179luYDruRIURVZqYkRRnlBWSWqLGO3ShwtLOXRycvCHc2rox/BppXi2TqHqtJclMQMep2k1w8AACAASURBVI6ehd+SgGKPp8/ST1i118MtFyWw83A5kkDEat7unEpGbDGZNHsTSwa0jRBiC4ixFBZWIkkiBX6DQev3kBSj0P/aOPR4CUWxgBqHrptIgQoWPRncv6I4j8cXfEBFUS7u8hLenjOSw7t2AOD3eiksrCQlxUFhYeUvcm3Phd/y/Kee+3x1oL/k/Xoqv/XvB+ArLATA5ROJ8xxHVzPxVLiA4DUwJRXN7aIqEI8VqMzdh6Znn/6A55Hfw/U5mfPZnt/j/fpLXW9ZUhlyR0+mv7+U8e36kRybSGqcE5tiJa+sGCtBhf8cd+45i76dmpMtCMFqG5JdIafcx5JPjzLurosZ9+5+csp9jP5zfVxuF1VuL/lKLD1nbWDpwPZsWTKZLhOWRoi39Zy5HiSZeFXAVz0myIkZpDw0hT91HcCbs56m3dDp9Ln9Gg6WaRSXV7F262f0vPsm4mKsHCsoRSIoWFTqCi5s5ZT7GP7+fmbeeSEl83cHhZRM4xe7v3+P9yqc2/1qESXsTmuEUS5ZRTJaZiCaGmnJSRSunFxjrJYVC4IocrndgiRKCAg0yXSEJ/elLo1Uu/qzr8vvrV86mWjbfv37NXTO3+N1P1O7JEkgt6ycGa9vriVXvCcbP/4PxwpK6dz6Wvrf2wJFMJncow2CabB76yYqivO4d8Akes5YFy57Fky9XM+nbyzi8K4dODOz6TVrIyTW5Z9zh6MbBlZZIjlQgvn+KI5X9622+ycz42MvRW4tnDv+ULMsZn90MJx3bhqR/aUFqcbCnavEi+bTqfoZv8Wv/VtGjf/flrMa5NOmTWPr1q307NmTSy+9FIfDQVVVFXv37mXZsmUcPXqUIUOG/OyG3HHHHaxdu5Y2bdpQVlbGli1bWLly5Vk/+z0hSAIFP5SS2TKVS7vWR8fPviIf60d3o7SsjIJyD6/+6xuGdbmLY4WlpCY6wsY4BPNiKovzUd4fRaA0F4BAaS4VGyei3DWCispKbOkN6NTEhlV388p9aUi+Um5tmEhhlcag5vGk2wVESUGJjaffK9sxA/4TIejVk0m/KDJo/dckxShB8bcht9VUUPedUF4/vGsHed9/w4Ypg2qsPoqSEg11jHJeML3BkHVTEMCXj5F4ESFjHECQVUzNi6nEYyIgen9/aStRft/ouklmTAYTOwwiYAQwMRi3cS6bv94WNrydMQnnLPpWW6jiLf2aclOfxhRXaWTGW9idU8mcfx7i+XsvxWlXUE2N4hIfzyx4g2MFpfxr3nAeWfgRPq+HpKQk/rroIwRDo+jIAdZNfIIOTz5L5dY5EWNCwcphpHf/G/cNnExccjqm5uXCBCuZSXVodEEdXnlvG/e3bk5WaiKSJDKt/eWs+eJ4uN055T4kuxIOrQwI0bSj2ggJTW2r/n0zmybhtcsE3C6SJI3ClcNqHauff6hltQGwnrikDDTdx5T7LiGn0o9FDopIDb61LpIkRVO+ovzP49U1er+wlHG92taSK76UVWP70PGWa4nx5rO0z60RVYOeeXMv+T/s5a3ZTwMERYzrXwKyiqk6uL7LIFo+2BfD0EFWOVRSjm6YuH1+mmcnUvjK0Ihn2LNmOAPb/Y1eG44y7t39LOzSlIBuMuGeS9BNk+U7jvLYDdmoJ3+BaoX4k41yu9MaVOmKRqxHOUfOGoO3bt06li5dSseOHWncuDHZ2dk0atSIjh07snjxYtasWXPWk0yYMIGbb76ZvLw8evbsyd133w3Ao48+yu7duwFo27YtWVlZtGnThvvvv5++fftSt27ds372e8KQTKxX2mk/6kVaj5jO2EVvcHtyGQsfuYWXu1zJx889yKj7rsKnafSb8SqGYUZ4zq9uWI+L0uLDnUOIQGkukiiy6qlO2AKVeHK/Y16Plky88wJefPgGhjQKMPWmGN4e1Jop9zTg5d43UXxwLyYCCAA6+MvCNcQDhklOuS9C/A1OKKjjKw8rr4cIeW8ihN9mb4ysZx4lys/BVwlqDFKgEDAxJGfEx4KsYvq9IEiYShyCr+C3aWeUPzS6bqLoVmRRptO8QWz+ehtwwvDWDO2som+SJGIRJRRdwFvuxxoXnJ65Srz888VdWBwqmRl2ZnW4nFYNnfS6ri6pDpU3vswhoOssfvtjxvVqy7vTBiLLMmNWfkhcciaG5kORYP5jf2bBE3dzeNcObHZHrWOCqziPlaO6U5p3hBVPd+XFHi0JFB3C4/Vx/63NMQyThcN7YFNVYlSRh1vUpUlm0AOSGW8hzmmh1aArEaJ5jqclJDTVZtg13DP+Ohp3voSS0lIeHPMShSUlpx2rITieLhnYAa+7go2T+uHP3Ydd0IiVDIa0qo/pdiNBVPwpyv88AT2Y2hkqcXYyxwpKKa6owi76WFpdfheq56sD24MgsHPTK9w/aj49pq0h7YJLUW0OJEFEcBcjG34wDP6+8Hnm9biJGE8+z7/yNs8seAN0rdZnOMMefCaT7CoVngCPv76bti9/Tr81X3NP43Qspwgahxbu7M7ggm10oTPKT+GsHnLT/Pk31KhRoxg1alSN9xcuXBj+X5Ikxo8fX+v+Z/rs90SV30fvkwTcut7UiGWndCDLB3fg8aX/4lhBKaIocHuLy+l6UyPqJceR4IgJhj8mZkR0EnJiBpVlRZTkHCbg87B62P01OqVOo+ZFvPfaiAfoNGoeC564O6J2+R1PjMOacQmZ8RYSVbNWEQxBCPoln3j57xQeOcD7Lz1LZVEecckZ9H/lE4yAVsPrHiXKz8X0ViJaHYhaEQCGZI8U2VJUzMrgCrSpJCB4C3+DVkb5b0EzArUa3pIonlH0rTaveIuul/LVmz9QXJ2T7S728vfpX3DxbVn8tWU2g9bvYVHnJrS7LAERIRyWOaP/gyQ6YpjU43a8+T+weGB7Hpr4SkS/XFlWhHKGMWHVqJ60HzaTxQM7sGxQBx5Z+BHHXSZ1khPQAgYTthyi87V1WP35cfrfUp/Vnx/nry2zUQwDnxE1xM+GrhvYnBaqCivR9QBFZZXMerIzcQ4F12l+lxAlOYcRELhnwPO8PfsZmnYfS88F77BoRE/0b334L1CxpdqQFTlaMinK/yyyJJKVmkhZlbtGiuftLS4nzWZiFWufr/qqKmjetjuLB7an78ItFBzej7POBbw962l2b92EMzObLhOW0urhwQCse6Yzo57fQIkmExBqn29b1OACa58b6zFkQ6Ti+ph39rHsoSsivJm6biDZJVoNujJaTjLKT+asS7MdO3ake/furF27ll27dnHw4EF2797N2rVr6dWrF506RWsBhwit8oVwxlpq7UACfh93tGiE3XAx75E2NLsoDbMinw2TB1JeXkHqQ5ORE4Nl4uTEDGLve5o350/EmZmNIIi1HlO12Wu8FxPnDP8fKv+weEA7RE8pMztcjkuXIrzgAE1atcVVWsCc7jcy4Z6LWTvhCe4fNZ+BK3agxGehS3GYliR0Oe60xrgkCUiBCvAVhb3yP4XzdZwofwxMbyWCNRYxUAwIGEQKHoU95FQb5L6oQR7lp6OIMlmniAJmOdNRJZUlvSaGPwuFsofyy2VTCIcwQ9ArvuPVb7m8TbAvtTuteCuD+dqxDRPDJXTMgJ/i8kr8gQBDX3ydlk0uIjUhFr3oMBXHvuP9l57lodFzyMyqy6BFm8lu2gKAN+dPJK7dyFrHBIiswFGScxi/10NyXCxaQKfKb7D1QAnj3t3PfU3SGffufob/+SJSVTE6WfyRKIpIUVkFzyx4g+eXv01xhYuM3nNJ7zkLS73GyIkZxLUbGf5dIBhJlvf9Nyx44i5u7tKfCzOTSU1w8MgLS0m4JpEnt37H3S9/Ts9Vu8Kl6aJE+V/DKiksGtGTtVs/Y1q/B8hKTQSCxviYtlextM+t5O7fVWO+6szMRrXZwxUqDNNg+9qXqSzO45auAwDCi5aluYdp8+gzPDR6Dk3rJZNmNZi45l9YO0yI6FuTHppKmWEhM95CvURbrYrrgVpy+XXdwGfo+NDxGXq0f43yozmrh/ypp56ibt26vPHGG3z33XdhUbeLLrqIbt268eCDD/4a7fxDoEhSxOpeSVXtJcNKPQH+1u1GCl/pS1m1kERSu5G0GzqNuHgb3soS0h6dA8jkfbeHVyYOprIoj96zNlBecLzWY/o9roi2ODOzscbGhV+HJm3BGuJ+UmNEkuvWodcp4m/tnprO3Eda1/DA91/2Cfo5hN9IkoC74DsWD2hXMy/9FANekgTwlaPrfiRJjfC4/5jjnA/O1JYovw6mtxLBEosYKAElHtOMXIARFAum5sc0DUwlEcp3I6ATWYAkSpRzw4qNJb0m1hBvk3WVzJgMNvabS8AIIIsyVmwn+gODWgV8VLsc4S0HwsJuTTJi8WtB0TVFlkhNcPB4h1vJO36Eva9OoP3wmbTv8xT+z99ASHGQWS+d/i+uYd2s8ez/9B94ZQfpjy3E1P1ofj9vvzwpLKzpzMzGXV4S/j/WqqKrElLAhYLGi3els+DLKuJsMjnlPgwj6rn5KbgDAR55YRmpCQ4W9LoZ/8ZhHK0ev1MfmkyZy02lZlBZFIyscGZm02P6WkRBpP2wmXy8ag7ths/i6W53IRuVpNk8TLo9jenbStiVU8Wg9XtY2qUpUvS3ifI/hq6bZKUlMeGxv6D4K3lrzEOYooyJyEs9b6Qk5zBblkym57Q1EKggJtaBu6oSxZGBarURm5hCk1ZtMXWd5m27s2pUTx5f8EH4+CU5h3EkpWEX/Li2vczxt3JxJmYwuN2zTPtgL13umMClWcl4dRi4ZDNje7fnpQebICFEVMiAYLpP1DcU5ZfgnJZjH3zwQV577TU+//xz9uzZw+eff85rr70WNcZPIVZSWTSiZ3h179V/fUPPWRsi8q47TFiFqQcofDVSSMK7cy0J8TKi5xh2q4Gs5SNJGqZq575Bk+k0ah6yxcpnb79aI5e718w3sMcnR7zXefxiNO+JOqehSVtYiE03MP0B7KkN6L/sE8b9/Rj9l30MQPthM8PeGTiphvm54CsPG9GhfYN56SURnu6QwT2nx41MuPNC5vS4EXfBdye84Kc9TvlP+WnOSKgt6yf1JXff/1GWdwjDlY+qRL0VvyamtwLBYg96yC1JcFLOLgQ95GAiBHyYagICBoKv+LdpbJQ/PCGBt4395vLpqNfZ2G9uWEk9lGduM2NRdGvk4ly1gM/J2J1WbHEqf+p3Bfs+OkrxoYrgOVxBYbce16ZxKLeItVs/wxFjZeADbSipcBGrSjRv2x0VHf/nb5B8Wxc4vIDAzoEEPhvO/QOH02vGOgStHFHSMASdD19fwLX3dCW7aYtwOOaWJZPD/f72tS8hVBZQ+N1uPIVHSar4jpmtE7nI5uXD7vWwVOUgG5WoihiNQPoRaIEAxwpKGdmpJf6NY04R2RuOqyiXdS88yYPjFjL63e/464LN/N8Ha9Cq8si6sC5dxszEZouhWZZJ4/ypGFu7csn3TzOrTQxNM2NP63mLEuV/AUHTCRQeYm6Plky480Lm97wJf3k+juRgpJIggDNRItX3EQmxGnXqpZOSGsfmlyew4Im7aNNnFKZhEhPvxJGcjiQr9F/6Eb1nradJq7YkxMdT8GqkCKN/4xg6tmjIveOW83Whn69zXby/4xsqvRrfF7qZ9uH3TGt/OZnxwdJlIZV1SYz2lVHOPz+7DnlOTs5PKkH234iAQEpCAque7Uupy0epR2ddjocnlm2joKiYgnIPI9fsYNYjt9UQkki8uQvo1V4XUUG0J2KKChnZWcx/8gEOfRUs29Bp1DwczjT6L/sY3e+j8MgBvFUV/PPV2bQfNpOYeCfu8hI+XjWHGzo9BhCRQ95r9qagEFv1BFPXTSRLPK5TvNGhWuOhchHnqqau6/5aQ+qrivOZ3rn5CU93QkqtBnf/ZZ+AHHfa4xi6dh7u2lPwlfP+vHHc3KV/RL313rM2EJPWMOop/5UwPZXVBvkPEHt5zQ2UYF6X6fdiKgkAiP5CDEvqr9nMKH9ATi1NFsrv03UTBSsKgE4w3kIS8OJBMwIop3rHqam8HfKK//uVvQBc2a4BZcdcuEq85H2Sw6wOl1PlcjF++Qc88/A9rP77p3Rpcz37j+ThEHUynKn4XRUk3twFQQygXjsaQbFjBLygu0mv4ySwcyTa/nwEWxp3dR/Lu68spuvE5VQU5pKQnsV9gybjLi/h83dXcVuvYeHaBG/OHE5lUR69Zr6Bp8Jg6eBOlOQcpkmrttwzYBIlOQdRbXb8HhdJWRcG05Ki/V2tSNURcClx1lqFoOKT0zm8aweSrKJYrDiUAPc+NhBMHf+B1zAPfIml+bNo3y7H9OQDYHryidk9niEtJ/H0Zg1ZFIiWLYnyv4QsCdhVD6Lpp9xTyMXXtabFXR2Icybjriqh/dDpzHq4JW37j0Y+tgGlQQf8X07F9AT7w45PjuHF779h6eCO/HX+e5iGwb0DJ/Fi71YnykfOWIeqWmp9brOcsWSlJhIfa8MZZ2f56EdIirVSrut0bZ5FYozMmDsvRpEEKjwBZn90kBfuvYSfV108SpSa/CzTxu/307p1a/bu3Xu+2vOHRhAFDCT6rt9PUoxCj2vTaHVBLF7JhmkXeHpOUPCt3BPAeaqQREodBHce/l2zwh2NetVT8P16ek+YxeJRAzn01Q5S61/CNx+/Q0aDRny8ag7N23YnLiWT2x8fw5KBHcId0P2TXsMlxPD4a7tw2G0oqoUmj03DllEH3R/peazNG716bG/aD5vJhimDTqipn8NELaTOfmpIvSU2juymLTi8aweLB7Sj76IPz2hwn+44v0SZNV3307xt97AxHmpLKFQfOe4sR4jyczFNE9NbgaiqiIYblPgaE9OghxxMvw8zJhiFInhzwdHovLZDEKKr3/9N1CbC1rJPY6Ra1MUlSThrDfKQgM+tg68C3aTsuCss6Abww45cWg++ClM3ESQBSREQYiXm97+DcreXe65viipLrN36GQM7/ZkYxYu/spiElET8n42L6P+179ejNOiIYHVievIxPfkE/jOe1g89R0FOPnHJadhjZOSUOFwWaNmpD/P73F5jYXXJoL9ECH/e0nUAlSV5rJ3wRHjbLhOWkmpPRBcitRuiBLGoFhaN6El5RVHN8TsxA0dKHZ77MAdrjIKkleDfORZv6LdsNg4N8O8cg9KkL/787eF9TU8+aXaR+fc3wim7qiMhZFx+G4Ho4kiU/2JkScAh5KFtH03Ak0+KLY1OA8cR2LcCY8927LY0lGbjeXrtDpIz0sEdH+wTm/RFUByYWiWBAytp2380sx+5B1FWEASRlaO6R8zllg7uyNBXPqpVwC0mxsqCYd2Z/Oq7bN7xDVmpiSwc0ZMEmwOXZpBT7qNeopViVzBKNNmuRBfOovwinNUg/+yzz077md/vP6+N+aNjGiYet0ZSjMLjzZNYsmkznVo1I+CLJT0pgXUT+xEIBLCqMpYHJlHx+tMEqnPQBEHC1H2oV4+o7mRW4/9yKkqTvpi7Z9Nn5kpWTRhKWd4xEtPrho3H3Vs3AUExtn5L/4k/oPPtkQKGr97BFweOhNu2afpTxCcnIOpGRD8iSQKG11ercZzeoBF9F/0DW0pdDEQU00Tz+CMmsZIkothU3JqBNdaK4RPpPWsDi6vV5UOTwrdmDOe2XsNZXL1oIErSmQ1uSzy9Z2+skUN+rgsDPwZJUol1pv56HvkoNfG7wTSQ1Op7S46raZCHPOSaF1NJxkRA8OaftyZUebz0mrIUt9fHxEf+whUNfn+lFaP8eGRTCBvjEMz33rbga1oNurLGnMqL5xxrkJscdh/DXeEld11l+NhJ9eO4sEUG/5jxJa4SL1lNk7jhATvaZ6NJ9OTjtKVRddnTLHxvFwMfaIPHp0F8Es6EGLS98yMmmtr365Gz78D/5eSgEbdzTPDMnnwsVgtJmfWIVT34PxuB6snHYksjcOlg4lLSKck5HLGwunhghwjhz/jUOszv0yZi0rpqVE/6L/0nmqIS0A1kScQqKVGPeTWSbpCRkoQ3LpaErtMoqk47kxMzSH5oKm5DIk6uQDQs+HaOxfTkIyZehtywMxga6iXd8FcdRlATIo4r2NJIcsQgaicME8GWhqPZc1RK6VGjPMofGkkS8OoaxwpKscgi8aof0QxgCDIIEtonoyMiRrTPxoUXrYKvx5LcdCC4j4AtvYaHXL3qKZweBWdmNpKskJCeFTGXy27agvseH4mkKKR0mUThqhPz7pSHXsAriPi0AI/eewudWjVj3voPefSFpawc/wQPrvw6HKY++6ODFLv8zGh/OVbB5ByTOKNEOWfOamp069aNlJQURDGaT3s2DN3AX+rj0RbpLNn4fri0zbGCUrJSE3l5eA/SnQl4vF6mv/M14x9biKhrFB4/BP4ytFO849rexcHJmScfOeCiy9CJlHsNLDGxNYzH3Vs30fap6ex32Xh6+ZYItfes1ETS463YxBPKupIkgL8c3e3G0AO1i8/lHkYPaCQj4MZCrhnPpclWJK8v6CWSRASrhe3HPbg1kxhFoHmmDXtiakT4fCj0/U8PDwofW5KtZzS4dd0kJvUi+i/7BEP/hcusWeKJS07/1TzyUWpieisBkORAsOaeFFtjG0EOBomZfg+IMsixiOexFvnMtR9w4FhwYvDCqndYNapP1Fv+38BpRNioRTvrdKXQAkYAhROh76Zu4q7wsub/3uPxbl34esURXCVeGt9Vn0+X7w2f79Kb49A+eyZiwhm7dxKP3zuTYr8dh83HofxSlFSZhFommqZsD75WHOH2CLY0DERiLAb+asMvdGz52xlhbxGcEPN0ZmYjqycWFAxDr3UBUtcDdBg3NzxmLRrRk8yEqMAlBCMjdEnikTXfMr5NfeLbTSErycH+48X0n7OZNSM7oe0Yg+X6KWFjXLms9ym/6XBMawqCLS38nrvRM8ToJvrOUw2T0dhv+BvlHutZWhYlyu8TSRLIKSvnkReWkprgYOmAW9E/fz686KQ2fxajOvonxKn9nenJR5CtaHuWo14zGt8pz4n/y6lYr3mezuMX4yotCvd3JTmHyW7agu4jZ1D15iSOv5VLzOW3kNZzDq7yEipLi8gvKsWW4uDJmSvDfd60fg8w5dV3EaoTf0Klzp5q3YBB6/cweEO1+GLE96w9JSpKlB/DWQ3yzMxMpk2bxtVXX13jM5/Px5VXXvmLNOwPiWSS90kO2W2z6NSqWdgYBzhWUMpjk5ex5rknyCku487rr+DrfA917AKm6cO/c0yNTkZpOhBTq0SwpaFXlFC4YTrKXSNQ7Im1Go+IMs44O7MHP8yAGcsjJlU2UY4wxk9WMG/Sqi09Z6xj6eCOYeP4oYmvoKg2lj11f/i9XrM3cbT8Ii5IUNGrvCg2NWyMA7g1k505HprHiWyYMqhG+0Kicr1nb0SX7Wc1uHXdDHpK5Wpn6S80KdR1E9WRXsOz/0t55KPUJGSQi5IfAoAcC3giNwqHrAffN+U4BH/JeTm/y+vj3R27ufu6pqQlxrHkvW18vu8gzS698LwcP8pvSLUI28lGud1pDUqanjJnCpVCq60GuUTN0PfHu3Vh/n9W0a7TbVwSk0F8UgwNO9RDihHR3Qb2RAPzQGQUh+nJx+XzUanb8Lg1vD4/FsWC/7OpNcYAy/UvINjSABO1+bMI1mRQ4zGQkDAQrx2N6SshcGA1RuleTE8+cRckh88VqsDRZcJSrHYHt/YYyofLplFVXFDrGHKsxBUxZj3ywlLWT+yLEg0TAkA3zKDismlwsCxA9xnLwtdLEU18nnzAQLClITfsHDbGIfSbTsbSciZqy7+hmybHC8oYMPs9Fg26m1hPzftENAOnNiFKlD8MXl3jkReCqZpz+t5FzDfPRz4PO8egNB2If8fT4X0EWxqmVhnxWrAmoV79DADqjdPR9r2KcfT98HEkUYzQTQrNZ+97fCRVb04Kh6m79/wTf+5+zFb90USF2LhkRq74e0SfN/TF13m+z18wOLEYn1PuI84mh/8PGCdqu/yYlKgoUc7EWUfZxo0b8/XXX9dqkAuCQEZGxi/SsD8igihw+W3Z+ASTpPjYCC81BB923TBIjLWTlWohr7icMSv/wdy/3oZxJHIwFqxOREc2pr8Myw3Tqdz7GYHSXJJT0ikuLqHz+MURAmRdJ61AF1Umf/ADyXaFN57vhxHQkSWJWFlFM00ISf2ckjMeCnvvt3gr5YU5VJUUYBpG2BiHoPdkyYC29Fvxb0QpBYtDRRDEsDEewq2ZKHHJ9P7bJhY/eaKcWq9ZG7AkpPLkin9jyo4ThvevYHCfC37NICat4a/jkY9SA9MbzL8VBR+maEOopZRZOGQ9ZJArcQj+0hrb/RT+8cVevH6N6y5vQGKsjeUfiPxz1/6oQf4H4WQPhVitFm6YJgHBJEBNEbaWfRoTqKWM4+lKoVmx1Rr6/vWKI7TrdBv3Lx3I7Y1vYuitT9D7pWXhxdAtk3sjVntDQwi2NBLsMQiGytRPj/JEixRkKYBx0jbhUGdBQrl+CoYJgS8mn/C0NhuHf98KjPztiGk3BFOdAh4EyYpsSgxY9DYfrlpEm8dGIgCi4UIV3dz56GBufuhJSl1+Ok9Zw+phJy+4bqTv4q0R1+NYQSkB3UCJVhYEQBaCSssVPqPGGG+KCmqLSRiiBcsN08A0UJr0DS+WANU6AEVgBBDVBOa98wUAFktM2GseQrClYQoKFlGKetyi/CEJ6Eb4GUmNt2EeqmXRKbZORMSI0uJ5BK0cy40zMQNeiEnD8JYiYARfq/HIF90PDbtA1SECh98n79ABbuk2gLikDFxlRXy6cSnth82kzkWNyXmrppBbnQsvY19+BYKaQGFpVfizqy+uxxMdbuXCzBRKPBpNMmLZnVsVfOY9wcWxzHhLRA75j0mJihLlTJzVIJ8+ffppP1NVlQ8//PC8NuiPjKkJiLKAarXgOB+bywAAIABJREFUjHdE1CSHYOi4KksUlFZgt1noXx0mk9v5Fuq2mIQgW4P547nbUeq2wffJoHAnFdv8WbRbumEKIra4RGKdqTyxcAsAlUV52OKTGLo5l2K3xnN3NcSmleMudeGpMtn/hZfGd18QXrGrTcF899ZN3NZ7BHrAz4Ypg3ho4iu1hjT6fT4UU+Dfx/zcWE8mRhEijPIYRcA0IT77Urov+gTZ8BMQVb7T4igrM2l9gRVfpY/fI7+WRz5KTcIecrxBz7dZcygLi7r5Qh5yB6LnSI3tfgr//uY7kuJiyUpJIKAbXJyVzrbdBxh6/x3n5fhRfjlq81C06Hop+z46SuO7L0CwSwh2iVaDrowIKQQTjUCNfOnT1SCXqT30/ZKYDLKc6Yy7dwAPjH45wtsy8KX3eLHPs/D5mBMhyk3GMnDDUZLsKo/cUA/D0IIGb8u/YfrLqvv/22qEOp8s7OavzrMM+EtRGnTAt31YRKh7pvlveo6bjFcTsVhVAntexvhhO7otDfs143n+3S/YdySfQaNfIzXeRnxCIqo9noLytyO+X1ZqIrIUTVcLESsIzPpLI+b/6xD9b0gPj/Gdb2uGoJWjVR1HsSaGc8gFW1pwscQ0EAQRM+DFNDS0L6egNB3ImC438vUxL4dyS7nsmnHo/zkh6idf8ywfzjuIt0KL8LiFcnIDukFBaSWyJEQXjqP8LpElMfyMFJR7SD1l0UlMuwFTkJGaPQ+CRFlRMbHuCoTdJy0+Nh+P9t3rGPnbT6R9GBqiLQXDlo7a+HGSdSuaplPhchHw+3CXBcuh6lCrkJvbr0PAT37uMSY81p67hs7m6ovrMazrXRFpptMHdmPtHpWHmmUx+6ODZMZbmNn+chyCgF69UIZ+7ilRUaKcibOOtIqioCjKr9GWPzyiLCAIAhW5bjbtLePl4T24vUUjFo3owYZJ/Xj92b+iyjIDZq1C03VS4x1sGtedrDgDbdcs/HsWgCyjXv4Q2J0ozcZVhyk68e8cQ+LN96Mh46ksZU7PW5hwd0PmPXobsmohJi6ZyfdeyoqHmqIUf8+cHjcy9YFLWDH6duo20/j6nYNBDxInFMxPxpmZjbuiBNHupN8rn5CQll3rNgo6kmnQPK4KV8FRmsVVkW4P3kahHHLN48cE9vni+MydxJdVDsp8wRzzsJc+SpSTCHnIBdONKceBWctodmrIuhKHEHAhmD9PXNI0TT7fd4irGtYlUO2Furx+BofziimuqDzL3lF+a2RTCHu/ITgZ2vHqt1x4XSbbFnyNbArouoHP0PGh4zN0wCSnrJwOI+dyw1+fp8PIueSUlSNVGze11iA/qf54Un0Ht/W7gPtGNqBBRixvD5gHplgjKur9Hd/wfVU8gRazKWm+lH0NJjHnC5PHrozlkUYKWRYvF8WW4v9kIL5tT6Ltnot6ceewkrDlxpkoTfqifb8O5dJe4eOannywpqBc8xSoCsoVTyAkXhoOdZez78D/2Vgseh7ap8NRGnRATLwM05OP/p+xjH/oRgC6TVvL7aOXo8l2VFFm0YieZKUGKxiE0p2sUnT8D2HqJhkxMs/cfhEOewyLRvTk9haX81y3m9GO/QO17g0g+FCvGYJ6w/TqyAUvCDK+Twah7ZqFIIgIVieCbMUqC2TKVWwcfDdzB3SnMKU34s3LOJg9ipyKeIoPVYY9brIpIEkCpW4XRYVHMb0F5OUepNTtitaQj/K7xCop4T7luZX/wt3omeoUnKAxrlz6MP5PBhH4uDf6zuE409NRBS/q1SPCc19t33LUpo9huWUmyhVPoP2wDtGaAKYGnjx824eiBArxlBdR4fKx95P3+fNjI9kwZRAvD+lMXLuRyInBSF45MYPUrlNZP2UQS7pdzZbRHUnUSri9xeU80eHWGmmmQ2atYMgtWcRaRJ6/9xLm3n4xia4AulenMteN6DcRZSE8LoQIp0RFifIjOOfEsNmzZ9f6vqqqpKenc9NNN5GcnFzrNv8rmCZsW/g1ljiVu7pegioLDHrg9nAOTVZqIguH9yA1wYEkwKxefyIz1UHg82cQUq5CvexBzEA5hu8HtOLNqEnt0Y68j3JZ76DAm2BiU2XKq0pxJJ9Q0l06pBN9F32EVlRGbFY8cwe0jQg1X/FMR/ot244Yo2IJ+DH1BHrNfIMlg/5yUkj5eirMGAYt3cyLQ7qSGJtI79kbWDwgUi1949QhtH1qOvMeaX1SrvUGrriwMboBmico+CZrOtdmWvg8xxcWfLs20wJaNCcuSk3CHnKjCkOqPQ1GEISgUe53B/eRg8Ivgr8U05L2k899MK+IovIqLq134rwXZKQAsPdwHi2bOE63a5TfA6cRbVPt8mk9FSfnNsK55UuH6o9/885Brr3XgvDNM5gH8gnY0pCaDCOnXKw1KsqnB3OPJVEgRhHp1sDg1QF3UpJzmCHL/46lcHFEXqUR8NWqJExMRtCoxkRt8hiCzY7hP4ZWsBxTK8FyxVP4v1qMWfptWAw09DdUsSOkVSKhM6htC7pNO0JWaiIWRUbTDDIT4lk/sW9UZf00iJKArsgUl3kYvH4PU++5iHHtryHgcxPT8F5MrRCTMrTyzahJf8H/1QLwlqI2H4eYeBlG6V78X7wQ1IdBRJJlHDadbs/OZdOc55j+8J9xZmZz35Q3wXVioTF0H+uSToJQQOaxSZiefFKrReF06hG1AKL83tB1M9ynmIApiZjXzaK0vIw4ewzmzmHhvk+wOhECFRGlf5UWExEtCmagDFOoQHNtRr20PaYgAyaiI7u6MlE5imjFaXdwXcc+vPTYbSfmx88NpMOTz5JR7yJMUeKDlS+y+8ONQHUq5sB2jF/0MQE5ptY004IKLwHJwoJtR/jrJWnYrSfGB92no1jlc06JihLlTJxzD37o0CEWLlzIjh07OHLkCDt27GDhwoXs3buX1atXc9ttt/Hxxx//km393WPqJq4SLyWHKnD6DCxioMakb9aaD5jx5IM4pAApdj8KLrAmolx8H94fhuH9vj/+43NRU9rjL96AfFE7TPyozYZjCgGkQCmWfZvpPnIG2U1bAMFOpSz/MK+M+DOVRcdxJKdHtKsk5zAev49PjnvQJBkCLnyuKvrMf4+Rb+2jy3NL2Tz/WQpKyigor0SWRLwBHXtCUC29/9KPaD9sJm/PGcnurZuoLMqLrNc9oD3ugiNhYxzA59VR9ADXZ6m0vsDK9Vkqih7A541m1USpiempBFlFNKowJftptxMsNgyvK7iPEqwPL/5MYbfdPxwDIDstKfxevVQnArD74NGfdewoPx5ZEjC0EhJiq4i3e5HP5v07yXMdwu604ncFTuupODm3MUQoX/p06LqBYJe4vmsdhG/GRxjRMbunEG/RmPZkhwgP88oxj9IkoQLLZwOJ/7QHdb8dRmqMm7iUYB8d50yKCOEEECWlFjGwqQiGhnzZI6hXPIov9wU8+7riPzYLNb03guLEd3wqyiWdw6JIJ4sjnaxcHFJpd8ZawjV3Y2S5+juaKMjYJBUFOWqMn4JpUyis0hi8fg8t6sVxkd1HrEXBFmvi/X7IKeP3GyiXdK4WrxoX1ATgpLxZ2YJv2yCsu4aRUf4qvSfMov4VLSjJOUydBBvxqTaufORinPUd4fvYofhqCGPFfPM8DuWnpYHJkkC83Xvuz1qUKGfh1HtKABRk6qQk4tcN+s99h93H/ciSENH3yQ0749857sR71kQE2Y/3h6dqzIsFWQRvKb7tw8KRJwmJMdjtNhRZjpgDH961g5mP3E6Fx0dllYtGzW/mmdXb6T//TbKbBp83ydTJqQyE++4QWamJlHp0xr27n/uapKM4VAI+nfJcN5+/th9dM8EknBJ1z/jraDXoSoSooFuUn8A5e8gNw2DmzJn8+c9/Dr+3ZcsW3n77bdasWcOGDRuYPn06N9988y/S0D8EJ6n5Gn6dQMCMmPRdfXE9et59E12fXciWF7ojfTsDs0lflMsexnd4HILiRK3TF0FyYBpelJQOiGoq3u+HYubkISjpWOoMJ/HmzuS/Np77Hh/JnMfvCyuYl+QcZvHA9nQaNY8FT9wdPq8zM5uAqOLWTPYX+anrOs7KUd0jPN8VxXmkxtvCIYq6bmKa1KqWXlUSWWqqJOcwFUV5JMjWYA52NT6vDlEDPMo5YHorkGNjgQCmGHPa7QTVhukJirCY1fea4CuCn+HE3nMoB7vVQlJ8bHgQtaoK6UnxfHMo9yx7RzmfyJKAw5KH98AoTC3Y5zmyJ1DpO3095pDnelstOeSn81ScnNsY4lzypXXdQECrYUSbnnzsisziXW+zdvzjaD4dOUZBMj0EPoss0xMqTbbxb88RE+dEPyWv0gx4ahwfayKmIiOqqRi+owiKE1PLw9Ty8B2dilqnL75DoxHj6mO5YQr+g++ES2cCEUa63HwipijTqEEqH814DM2w4/VFJ4/ngmkCmLSoF8ewK0zyF/YhpeMQ/HlbTozdeiX+wg0oSbcjqvWxtn4lmIIjyMEIB28JiEqNknXK/tm07T+aFWP6UnTkAPmCk6eXb+GlAd3IdiYREExs6Gi13HvyT5CPCj1r/kPn/qxFiXImznRPAQgI4XLAc/reReOT+r5QNE8I5ZLO+PNfrfW5wgxgalUn9BlMA3QfsTYHby2fR8en5yAIIqV5R9iyZDKVRXlUFeWRkZaCb9vLVJXmoiRm0H3kDDYsmEqOy2DejhymD+zGkFkrInLI5+/MJ6fcR6JdISZWxaKIfLZyH64SL/9etofbBl8V1GYKNTzalUb5iZyzh3zbtm3ceuutEe+1atUq7BW/7777OHr0f9ubZEpw02ONsTutyFYJSQxO+q6+uB6LRvRg1oAuDH3xdVITHFhlEdOTT+DAasTYzKAxnt4b//G5eL8fiP/YLATJjqGVYGrBEjymlofv+GSU1HQCpbk4EpLDBvWWJZOBoHGcWv8SmrRqCwQN6IdnbuSgFjReMoTycGmv0Parx/bmjr+OIS05KbLmrCWe3rM3hnPJQ2rpOze9EvG9Q0a6oWu/+DWO8t+J6atCirUF/xdtp91OtNgwvNUGebWHXPAX/qxzf3PoOJfUTcMwIieh9VKT2H807zR7RfklsFs9+A+Piujz/IdHYbd6TrtPyHMd8lC0HnwVsak2rurU8LSeipNzG6++uB5vjXuYDyf2wClrZ8zHlSQBr2GG8yBDCLY0Cqoqad/oNr6Yuw+rzc6Mj4/jtAm1Gu91L23CY1NepWL7RuQrhoePJ9jSEBT7iTzLxMuCucjXDsT3w2A83z4U9oqLMZeHr5EgORCUdIyKQ/i2D0Op2xotbydG6d4T9X7lVJTrZiCio386BP+H3TC3D8Bm5EY9o+eIIAAIDGwWR+HK4QRKc5ET0oOeu9DYXe3JQ04HE3zbh+H9sGcw17XpkygtnsfwldV6XySkptF5/GLef+lZnLEWjhWU8tfZKwhYdGy4EAWh1nsvGMJ7eiRJIFbyECe7iZddxIkVOGyuH/2sRYlyJuy22vvvOFsVgcpiJEkI52k/t/JfCFePC9/PJiJqi0lYbpyJ2vxZsGee/rlCQNs1C23PywgI+P9vGr5tT+L7ZBB39+jFlx+spTTvCBumDOLegZPoOWMtqixQtPrpsMBboDSXqjcn0WXkbOwJyTx3xwXUTY5j3cS+fPC34Ux4sifzdxaHVdYTbQqTt3xPjqaHlZBcJV6M6OJVlPPEORvk9erVY/Xq1RHvvfbaa9SrVw+A0tJSbLbTT6T/FxB02P3uQVoPvoJ8vYqxizcwf+jDjOh2N+OWbKKgtILUBAcLet1MoPBwMHSwdC+muwAl7WF8R6dGGt+HxsIpglWmlocgCsiJGcRn1mfwki0kORO57/GRZDdtgTMzm4JD+7jj8bE8uzWPJ5Z+jCU2nguUChIsAopZU2G9JOcwKfUaIloSatQBD9UKH/3+Qfqv+DelCRdz++NjI4z0zuMXs3PTK8FSYVGi/ARMTwWyNSjaZgin70cE9UTIOqIVU1B+Vi1yLaCz/1g+DeqkYpqRA2tmcgKFZZW4vN7T7B3l5yBJIhZRwoKERZSQJBFBCIT7wBCmlocgnFl74mTRNo8ewKMH8Bn6acMGQ7mNb77Qn9cH3IHz/VHkTmtH/oLe2DzHT2uUe/EwYO2LVDUaGmlEXzuONV9u46qsDG7vn02C6qbrtZkcLNFqNaACeT9QvGIQMZdcjyHEoDQdGBZw8+9fjXrN00HRo8t6Az58h8ZEjg1Hp6KkBkOgBSUd0/BiqfMU2r7V4fq+av07sbT8G5brXwAlDvwuBMOP9u0rEZ5Zbedo7GrUCDsXAh4fKbEKVskIT+xFi6Xm2H10KqLiwL97wSl1l8chSjIE3LXeF2pMAv95bxUdnnyWC9MSeWNkF25qehGxnjwKXu5N7isjkJsMi7j3lObP4fKfvs+UJAGb5zhlb01GLz5M7oJHOTalLUZV/k961qJEqQ1JEpDw1XpPGa4ijs/tgcOTR2pCMJzti/1HGL36S8Tm05D/tAJBtqLtmhUMQd89F1GJOf1z9dW8YGRIw874v5wc2Z99Pp4/d+tDTLyTkpzDrBrVk9jEVFLrXhihtg5Bo1yUJPzuKjqPmcsNj02g48i5GAGNt78tDRvjU9tdxtJPj7D1QAlPvbuP+ncG5792pxVBjC5mRjk/nHPI+oQJE+jfvz8LFy4kLS2NvLw8ZFlmzpw5ABw8eJABAwacdv+DBw8yYsQIysrKSEhIYPLkydSvXz9im2HDhrFv377w63379jF37lxat27NnDlzWLVqFampqQBcffXVjB079sd8118eA7wVGhV+P72rc8d73NmSp+at4VhBKWVVbsZ3+RP+jaMociSR1n4oga+n4f9mCZbmIyJD1vVKtILVCIIl4hSCko5pSqR0n4lWlkfJmjEEqsNveo6eRaUmsu6FJ6ksyqPfkq3M69UqHJr+8MyNxKZl4szMrhGGLlvstYap6bqJZEnAYlMRRJEGApDYiL6L/kFFUR5VJQV8vGoOd/YdD5b4aKmwKD8J01uJlBBcHzQE62m3EywxmF4XpmkiCAKm4qDi2C62bhpCkzadqXPZtT/qvEcKitECOnWSE2p8luGMB+BgbjH166b8qONGOTO1lSpr2acxpk0O9nEnTeqCfd45D1XnjK6bOCQfBSuGRnhNyra8TNJ9T2EKBogyXsmO3x807DUjwHu7t1FQUcKYO58gKz4JhzUeSUzkuZv/Qv7Cx4Je08QMLus6jWlfGYy4bjzGf06UwZIbDyV/wxwCpbkUrnuOun1fwvvhXyPaplUdRr1mNL5PBqFeN7r2saHaK26p/yympwr/N4swS78Fqutd+yvB8OP/ZiFy/XsJfP40erVAnOYvjaiNLRp+pIAfLPHRvPEz4PfrWEUNRDlcTsn0uTC1PMSYy1FSO4d/I9MQwFcaVItWHMGSpgdWAwJiTDpKi4loO0aeVOZsLP9a/xp3dH6Uio0TKSjNpV6jW5jUfShGVSGp7YdQvGUZ+etnk3xHf9S0+oiylTK3ignESh5EQ8MQFTzEhH9HG24KVgwl6Z7BFK577sS9Xl7yqz1rUf77seHG1AK13lOi3YnkSKJgxVDGd5nAveOWA7DvaD6Bygok/IiKt1qgLficmP7yWvs9TAMjbzti4mUniboF9zFK92J68lFVBXd5cKE+qLF0DFvd7FpLoOmCxGOTF9UQ+HxjYj+G3NqAA4Uu1n6Zwy0XJXNvk3QqPAFsdhW708p1D1+GIBENU49yXjjnnrdRo0Zs3ryZr776ioKCAlJSUrjyyivDJdGaNWtGs2bNTrv/2LFj6dKlC23btmXTpk2MGTOG5cuXR2wzZcqU8P/ffvst3bt356abbgq/165dO4YPH37OX+5XR4TGd9WnqsIbfrgVWQr/P2/9h6wa0p780lwCpbnkb5hD8t2DUJPqYKCgpj+K7+jkcO6Npe5wUFIRlHQExYmS9jCiJQsTATE2kfIP5kdMJCs2TkRoM4TDu3YAoGv+CDX25YPa8eTy7fSevYnF1UrszsxsHn7hDQTTgSSJNTxKkiQiWC1sP+4Jq6U3z7ShONJJkK3EpdShw9PNSUjLoLjY9Ste7Cj/TZieckQ1EVNQOVO3JKi2YD6m5gE1BrcHyo58xRdv7+aLt5fy0LS3yL7ixnM+7w85wXD3lPi4Gp9lJAUN8h9yC2jFpT/uC0U5I7IphI1xIFza6bahV2LJnhAOexSUdNTsCVR6bfzYkomSJGDDXauREkI0tIgJmqVeY+JvfIDcBY+GDevUrtPAkYWum6QIAp8OeJHcqlLWfLGdfs0e5h9zD9Kikw/fe0Mi+uOiV4fS4S9zmPSph6HXTcYqg5b3A/kb5uA78nV4O1MQg2HH1V4eMfEylMb9wDRQb5wFklBjbFCzxyHICVgbTMc0TALfLQ8b41DttVcd+L9ZiNKgI9rehcAJgbiQ4npoWz0QoOLIPqyp2cjOeoi6ERUlqgVJEkGHCmJI7jKZolXD0cqKEB0tUVPahz16oYUSuXF/tM/HB4VbL3sYtdkIEBUMnw9RsqDc9CKCtxBBtmHoGrd17knJx+sIlOZiqdeY5Nu7of17cNhoT+s4nPx1szj+8pNkDXsTS6wT012FzXM8vLAkJ2aQ2m0aHlsddN0M3+NSTFzEvV6yeRlpDwxFK5j2s5+1KFFEQyNQ4UZJjbynLHWeQts1j4zOT+MrqSTFmRrW75jaqw3aka+wXtUawdQxERBs6ajNR4OgY6n7NL6cBRiV28JzYhMlGD3UoAO+7cMiKlFoexcHNRoEOZzG6czMRvO68ekmmX3mYnrL0CtKKNvxFuJ1D1OJtVaBT63KT4xcRT3JQ58rY5n671w+PFAarEXe4XKu63EZkiTWSHWLEuWn8qPqZAiCgGmaGIYRfn0uFBcXs2fPHu655x4A7rnnHvbs2UNJyelDTdetW8e9996Lqqo/pom/KQHBxJEaQ6BKD6s1llW5w3mKT3S4FaN6ZR3Ad+Rrjs9/Al9BHgJGeMIFofCcyeAtRr1gJmqdYF6559uu+L4fgCIWk3j3ACz1Gp84f2kuFjXoUXdmZlN09Hvu7vdc+POSnMPomp/4tIZ0f+HvDF31Ld2e28y+v+v8c+7ucJ3yk1FsKjtzgsY4gFsz2ZnjQbSo6HIcpiUJXY5DFKMlV6L8NEzTwHSXIikmphyHWVsN8moES3WeuSfoJT/6/SGcSbHcP+E1YpPS2PT8I/irRd/OhR9yChEFgdSE2BqfpSQ4kCWR744X1LJnlJ/FaUqV6RpU+tL5f/bOPDyq6n7jn3O3mUkm+0rCjqhsWqxgRVus2mqrVnGr2qJVNgVUUDYXZNFWFhFcUAFRFK1aFWzVVvvTVlvFimgVEFAECUv2PZPMzN3O74+bTDJkgtiios37PDwPmbn3zp05555zvuf7ft/X3/cBjCOfQut5338kMtVC0y1fPoq9C8+lfHk8FV1TBWmBCJpfUDj23tg4mj78ivgsYk0J5U9MIeA2Egjvo2LZGKyHriL/5buY9cNfUPR6OY3VEfzJIiEdsk+GwbtF9Vz9YiWhiEr52kWxYByaMzS2QB98CyKQh5LRD+2Y65FmPdKsReBCuDhubhB6JsINE9kxifC2XxH9fAr6wJEo+cO891tqxoWBMWgi1tYVsWw4NCuuG+leffoJd+I7cQGyvgzfJ6/ii9Ygo3XUuc3BZydiaGF1lJY0sas6zJK/bSdl5H0oOf3xFV6ToOTsNnBqwZ+BcewozIolhD8dSWTH9Qg1jLnrRUS0Gikl0XdmYL45lui6KWSceCZdxjxEl1/fgf1RPB3X/mg+2WeNR8vogsBFum4sAx7XZ1dPIUCzPaTup3DsvRhZ2XF9Pbp7M5V/fgq18G7c3FXoPe/vFHTrxH8MV9FxQtVU/vkpjK534u95H0bWBMyPVuKWrsPcMBtFmIjGOl6cNZ6iJ6fRp3sOwUE/gMZ9mJvuR9qNQA2Rnd7YFtk5FSNnBEpS/9Y1MS7GwHEJnSi0Iy/HGDqXdS8/R9HGd8ks6MFldzxKMCOboBHGfm869vpJULSMzLPGYqYW4krRTl39jBP6o0aKeXD0ydw34khWXHUyo/pEGdQlSHFdlMlrtuCkGHz86i4EAt1QiKpNNIp6omoTutE5dnbiy+OgM+Q7duzgmmuuIRqNkp+fT0lJCT6fj4ceeog+ffoc8NySkhLy8vJQVRUAVVXJzc2lpKSEzMzMdsebpsmLL77IqlWr4l5/+eWXeeutt8jJyeHaa69l8ODBB3v7AGRltV90H0pkZibTWB2mfH0VD143kmvuXc0Da/7GsqlXAFBVH2J3nUOvXy2k8smp2DUlJA0YjpHbBYGTmJZopCAUhciO/WoIi27D33sJGWdMoHTFNYC3uGuorYzVdb903y1cdvujrfdX0APD70eRgnUr2gvwqUIhJyde4brJcmPBeOtrElVTycmJl7be/++vG9/k538Vn/1V99f98U39fk6ohgbpomkOii+TYLLHugkGfe2OjaalEQGSlCjFez+loryWo/qmk5OXzU9Gz+D5313LR396kJ9PnHtQn723ylNTzcgI4sr2C9H8rDT2Vnq75990/94fh9v9fJn+Gq6PxhwpWpCc6Uf3qaSmemOQ0ixJYXQsut8h7IYq9i1vH6QUTliFmpGBrP+c6LpbsVsyj+dPo2zNPajBjHaBtZqShaq7oERjtOHo7s1UPTmNI392D3s3VhFplAnpkCgat/+8L0elWOgK+EfeRVlz8JQ0YDi5I25ECAf0bIwf3gsICO2NefH6Tr4XVBEb+9WMn2PkjSSyY/J+88EsfAPnQZ+LkHYEfBkojglCASN+sSkCeQhfOvox18VshkQgj5wzb8ZqtFFVGz8hXH86mUntn8H/BIdbX4UvP74GNI2/LvuI/lf1R7oOP/7+AH4+5wn+fudVOI31iedvPYB+1KVE9y38PQARAAAgAElEQVRs117+vktwGyoQgew42q353mzUflNxQhUJhd+MzC7kXDiTqpcWk3P+zWjCSbgZpAmH7Oxk3PpyzB33Ed0c39edhiqSf3AV//5TNX1/kEJSik16kgm+dJDgNNYgbROhGajJGYj/YNP9cGz3FhzO95YIX6a/ft3fTbouTiiKm9eHjJ+ORwgdTBdcgUB4HuThMlR/gPqPXiVz2M8w356F78R5uE1lmBuXoA+ehqLrRHbd3K5u3Nd7Hti1SKcBpIO0GhM+GyKQjyt9FPQZxLWPvkFTXTUvLrmJiQ+swXr3xrgA3n1/Nsqxi3BEgBUzrmRMc5lp19wM5v76dB4ePTxO/Pj5GRcybsFfmVgSorguSjhsc/Sp3VE1hZ11uxn96Ez2VpfSNTOfh6+8nf5d+qBp/335x7etn3biP8dB95Y5c+Zw8cUXM2rUqFhmfOXKlcyePZvVq1cf0pt67bXXKCgooF+/frHXLrnkEq6++mp0Xeftt99m/Pjx/PnPfyYjI+MAV4pHVVXoK6OX5OSkUF3diA+VQT/rxa73S1l7+wRs18Xn0/hsXwU3L3ue3PQU7hv3c7qMeRAUFVWEsD5+AP171yWkrLvREIo/OaFQBjj48wrocdMa7KYmFF8QV9GYvPL/eHnFPBoqSz0BGbxgfNQ9L2ArQZAy4WLYkS4VFQ1xn+MP+knSRVxQnqQLHNuhoraVop6Tk9Lu3K8T3+Tn7//Zh2oA/Sr76/74Jn8/p6J5c0hGcEQy4VCUYNBHKNTeW9fBCxAaykt45//eIDnsogiXhvo6Atm96Tl4OK8/upCjTv0VqTkFX/jZW3cV0zUnk5raxOUWWSnJFJVUAXyj/Xt/HMr2+ib6q6oq7azKTh43kLBtE6ow/+vvlyoiCYMUx4ziNlbhvHtrfOZx0wK6XLEY1yIusPZ1H0je+ddjrpscC1y7XDoLxwanphQn2euPH71ayUmXLkaES1GMAK4ZhmABESOFXuyiesU07JoSCq9/gqyzb0BLz0NP1jDfuRHhz0Q78nKU5ALPDmvHc+iDJiD0FISRghsp8eowg8ehZ5+LtCoTzwdWHeYH8zAGT0c2lhB9+/rmbPkcLMAtW9dM7ZyOG67A+veCeDGkD3+H0m8qexac10x7XkR1pOC/rik/HPsqfLn+mpOTghV1aKyOIGwXTXX57eqXWXztL1GljfCnJ5y/pSUQvtSE7SWtCoRuI50I9pblyEh1jHarpWZhln4eV8oAzZspqo4eTCb9B+cgXQdbqgk3g2ypIg/Q16uKImx5N8Sxp2mweQbRLWUxkTjbVCl75LqEFPhEUFXFY9i5gOKxBTMzkw+rMbMtvq757pvor1/Vd0vUxo7jtrKRVk9BTcki/6IbMN+5NSGV3AnVkX7iuZjrb0L4M0FoCM3v6VgEspHSSjy2OREiOyZ5z1WPOWCkJXw27Poq7MYmstOCPDL32liWXFEkdoIAPidJcP/6Ei4fkMSaWy4DVWNvxEcoXJ9Q/DjD8H7/gjQfwTSDD1d8zA+u7BcLxgH2Vpcy+tGZrJl4Hz7nP9hNboOve13WGfx/szjoLc9t27Zx5ZVXxtHUr7jiCrZt23aAszx06dKFsrIyHMdz6nMch/Lycrp06ZLw+Oeff54LLrgg7rWcnJxYvfpJJ51Ely5d2L59+8He/tcCTQo2/OET/Kk6/U7rjhFR2fHHvURthxvuezqmsM6zN7BnwXm41Z9jbXsM7chfgbQTUtaFLxnXLEbo+XGf5SnrOkT/NhJr/XRUGql6eTFu1W4aX3+IMy8dw1V3P4svEGTmXz7n2lVvk5R7BI4jY769yZmeeFbhMdmcdtMQRLKBP+iPoypaYZOhBQGSdK/dW2rIrfB+6u+u69mqiHqCaviA1kFtoaoKjq4RVVUcXTsomuR/ck4nDl/IJq90RRDGVZMPeKwS8LIEbkM1n298H1+aN4YYeDT17511BdJ1eeORL86QW7ZDUVlVQkG3FuSkp1BcVdtOgb0T/x32tyr78eTvdWhR9p/AVfRYaVALtIwuuEJDkXbC7Eq4zqQpmkzeyEWxc7PPHIu9KT5wNTfMQXEqoWgZgWATXY/Jav5SFpUvzKd42TgqX5iPImw0q5HQ09NQU7LoMvEx1EAaRv4R6OkZmO/NQvgz0fuNwt71Im6oCIlA73M+9u5XkE49kjAipTtGzzvRc39JdNcspFObcD4Qvkz0QROwtq7wqO4t97t+FsaAMZ7i+rC7sPb8FSGUhL+BnpFPwbhlZJ19A7WvLSNJNOEP+vGnBNrNDf9zUKDwmCzSdJWsZB85GSn0zfJhVe5GaGri+TuQhbTCiedvp5bovvkITcEYMg3j+zcihY4xeDpC89Gw6R9oA+PV/I2hczA33Y/17rVQtAxF1mGqyeSOvCvWZ2MBNEkd9nWJJK1LEkNGZMPmOe1U92koiQX4akoWbkMFQVmbcG6PCTQu/pCXZv2Lvy/+ENnoIDtra78z6KiNVVUhSY3iNlSQ9+v5dLn8d1gf3pmQSq4dO52q11ahaGps3EM6SDvSbN2nIvSkhM8KqrdWbWGXYFViDJkV92xog6YhjTRCm/5G3dq5XHjjPDILenDh755CoiZ0NQDBTzNrWXrFMOaf3YcVo35EUsMeQrYScxJqQWZBD2pMwY/7ZrLsl4MwHUnvc3qBTiwYb8He6lJst9OtoBNfDgc9u+bm5rJ+/fq41zZs2BBTPT8QsrKy6NevHy+99BIAL730Ev369UtIVy8tLeX999/nnHPOiXu9rKx1Utm6dSv79u2jV69eB3v7Xw+aVdYjDRauLfn8/VKyTs8ialrsLa/hlotOxnzhtla7lJRMjIHjUPQggo7sfhSsssfxdZsaG6haBGOsT572jguXYW++i/QTzqHiudtJ+f7Z1L/wWzJy8okYabE675bd7baL4V/ccSLHXt6ft0sivPZ5E+v2RRB+X2zh5TguMhJlWKGf03slMazQj4xE4xbOqiowyz7rsF6zI6iqQrnpcuXvN3LWsg1c+fuNlJvuARd9HZ2TiG7ciW8HZGM1iuER26Ry4IBcaAboPqJVJVSX7CEpqxsAenNAHszM4+gf/YKNf32Kkk8/POC19lRUYzsu+RlpHR6TnZZCxLSorDv4uvROHBzaWpUdyKLsYKGpgrTkCOnBEGpAkHfVvQmDFFdoCRdnTXUOb9y/CSfYjfyxK+k67U/oeT0TUyP1lJi92Im/LuSU0T1peOth8i6ZTc9b1tL1mvtR3BAGLmpKFtmX3YkiFIqXjWXPwhHIaChm22PtWIPe53ysTUsRbhRr5xr0o0dgVi0lsvNqojtvQGAjhIa0SrHKn2o/HxROJfrenZjrb/NEjVwbY+jcmJWaVHSsjx8kum4KWsFwpNXQwQIVqv5yH1Uv3U3aSb9EKLBuX+K54X8NtpAcd0FfPn51F36pMenin9LU1ETN6w8jOsrsWXWIpO74esyJb69uU7HKn/KOcesJf3oF0bK7EEk+XN2HRJJ95ihkUi7GyXfhP2M1vuGLkcKH3ntErF2tbY+RpIZxAq19Nnfsylg2u6O+jutSunwUTl1Jwv6t+D2tjuDxvyD34jko/iBmyXZqX5zfbm7XpIgxXaBVoDESit+078S3Fx21sU8TKNLCX9ALPTUFpNWuP+HPQEnvjpKSRe5FU5GaH+3IyzH/vRBz+9MQyPVYPUJDStqPbd2mIp1WNqf3nLlYnzyB7+TF+E9/EnqMo2zNPZQ9Oonkfj/Erikht1sffjTzaW56ej2WKdttbmkDp+Da8PyMC9tR05N8Br9ZvDbO3vc3i9cyoFc3xg3rwbhnNnH2ig1c9/fP2Bu2OWPQD+O+ctfMfDSl062gE18OB91jJk+ezPjx4znllFMoKCiguLiYN954g4ULFx7U+bNnz2bGjBk88MADpKamMn++p4A4ZswYrrvuOgYNGgTA2rVr+fGPf0xaWvwi+e677+bjjz9GURR0XWfBggXk5BxmVkTNKutvLd/MaTcMJntYDpfctpSF4y+ma24GOan+OCqkqouYSqTvR3e3qqnnXgp6VxTVD7j4ut+G64Qwuk3zMhtuBLQMSB+IkT8sZqkiUvvS5cp7wB8k6+wbiKIhSRwUO46Lg0dJf3dfe9G2YYV+nFCk9dhQx17MAZoofezGdvWauWNXEqJjf1RTUZi8ZjPFdR41uUUs49HLjkHtYHHe0TmrL/9yegKdOHzgNtWgGl4/lUrH/aUFSiCFpjKP5p6S2xMoR5etAfOA0y5mx/r/47UHb+bXd7/cofjkjmJPrC03I56mJaXks1qbyrCL5vcoZ7tLq+ielfVlv1onviZoqiDFV4q5K16dPe+aVQg7iiu0mMp6oxkgZcjtWO/NjNEq5YBZ/PvJSk9YzpQ0EcCnqIRKKkhKQI2UlkcjlOEyL1hWFLLPHIXwBRBCglDQ07NxhU3u5QsRlkXxE9Ni6tmKL8lbHPpz0AeNBrsBY+jNSMWH1vOMWM1xi5UWbgTUIErKyeBUI5H4e88HVKTiQ4ZKYvdmfP8mpNCxPryrlTY6ZDbawInYm+9HSS7A/HgZxpBZmO/NiaOWmpsfIPus8ex7cDwVz91O3tgVsbnBpypEXUkw2Y90Xayw+T+lxO44Loah0/uivkTsKGPmr+KeMT8jv6EKhIqScjJ61hmtNeRVryLDVdifLEUbOBFfz/lIaSJUHZwm9PyrQBgILRVfz9uxyp/CLH0co2AMQlWRUqCIKNE2fdrXYw7WtrW4peta6cCWTWhPKYEUBV9qAFNLxmm26UvU142hcyl/4W4CRwxBS8vHSdC/3UjYcxz4wQWUrJwYo67nXDiT2teWk37O9Na5vQOBRtfu3CT/ziBBG/tTNdRIGcKpxWxmERkn3BlHJRcZR2McO4Zo8VL0rDNQk9IRiolM64tx0p3gNIEbQqb0QFGi4EZxhdq61nUaMCvWYhSMxt9nSetzZYXR+5yPufF+9KOvQM3pQdbpv6HqtVWoSaloGV0wHcniP77L9ItOAulS+drvST9hHFowDSdUR+Vrvyd45uSE1PQsw2bFP3fzo5lPkxn0UR2KMvePHzB3TG9ueGFb3PpzytptPHDxjXy8b3tcDXlQDWL9D42PnfjvcdAB+WmnncaaNWv4y1/+Qnl5OX379uW666476Cx1nz59ePbZZ9u9vmLFiri/r7nmmoTntwTwhzNaVNYbqyM0CqiPmOSme4v9J2eNJU2NEmqu9co6/TeY781upfa4rmdl44ax6t5Fz8wlsuOmNhYqc5BqGmbpoxg5I5BWLUaXYzG3v4i78w/eRHv8LKpeXUHGqaOxastI734kUqnF1TQazUBi9VQhEoq2cZAK+tDeOgi8oFyRB6bs2K6MDWwtKK6LYrsS9UueYznuwXfmThxWkI3VqH6vHMUV/i903BGBIG7ZPjTDIJjTA6Lr0d0GWvaejEAyx5zxK9Y//wDb1/2FI0/6ecLr7CyuQAjIzWi1PDMdyRNbG/m0xuu7TliJHdsZkB++SPaHY8E4eFkUs+hWtJ73UWe2bLh4Hct2JA1qPsFh9xGta6KpzuHfT1ZStavBK+NR8OokXdiwtpxTR83Ffv+2djWRAEreMKQDmmaDlo7rRIm+Mz0uEFb8eQhh02XkXJxQHcKXgrn5QfQTfofQTKJFM+ICLpLykaVeMG7kj4qz0jJ63YlwGoju/l3rOd1vwqp9FuP464EMkA7W2zfE00bfm41+zCS0/leDAGPgeKTQYrXq0mrA2roSt2Yr/gHj8HUfSHT3ZizHJd0n6OtrwIdFuFZjm0wjV9SRJGxUVQcj9X/Cu1xVFfaGLSav2cKcn3Rnb3kNz727nVm/Wogr/Rj5lxPddVvcnG198kfcmq3Ynz6JduwNCCqJ7mzT3t2mN1Pdq/F19zZSojunx70v9MzmmnOPrmv0nIAdrUHreykoBoYP5EdLqfz4TbSMLuSNXIQT8Gr/W/p68rB7UaSNKzRsy0VNSiPrtF9ibl7qbcQ0K1Z7NeRzQUsh/7I5VLx4b2wTKX34FQjNIOvMCUgFcJp/GIWEmjSKJlqP6cS3G/u1ce8Tczn+vGwU4eCGTIQ/02Nqfvo4xuDpmP/23AH0fpdjVj0fZwmopJyMkT+SaNGsNn9fTmTnbXH93ixZ7j0XPediFj/can3Wcy4y4mJtvg+3Zitm/Q58J87DKVpG3vnTsCOQet4tlIdM7rxkKM/dfCHvZudz5cwllK/9bWxzyXfRPHbU2mQW9IgLyjMLerC9pIrhx/VnwRN/5oNPd8feu+1KJ+H6sy4cZs55E0lPSqVrZj4pagqW2RmMd+LL4UvFML169WL8+PFf1b186+E4LoamkZzpp6nJJj3Jz4yRZ3HDfU+zt7yGM08YwD0jF1L5xFTUYFq8yISejFADRItm4+u9gOjOaftZqMzC13shRsFozOKH0bPOQNrpGD1/TmTnH2K1jeknjKPmbw+Te844zH9NiU2yKUNup0FNYGkiZULRNr4EBVyqejtRmaT+wxGKQqqs79ADWFMEBWm+uAGuIM2HpnQ8kXd0jhAQVVU0RWC4nR663ybIxhrUYBLgIEXgCwNyJSkVw/2c3G69kELFwUCTrQE5wBE/OJNP3nqJ1x66mT5DT0fV29sn7iyuoEtWOlqzl6iUkqe2NbK9xuYn3X3k+iX/KklhPfDqR0WcMqjTi/xwhRAdlfwk3hS0HUmDGUC6Bu/8fjP+VI3TJ/QmLVcHJYShqQg7yuCzcmgyM0k+cTEyXIPiT8bashy3ZqvnhXv05ZjvTsaKBevTY4vTlkDYOPleZLgINTmAYvjByMLeXYPwBTCLV8Ypc5tlq/EVTkTo+ej5o5HSxNd9Rky1G6uM6N4l8XPD7jsxCid4c0TBLQg1PTENObkA6URirCzjhDuxNi1tnx1tLCbr9N9QvnYRPk3hpPQGGmsqqCrZzRvPPsIZ427l1YduZ9Pf/xgTC23RJ/kuoy07y262Sjrt+AHc8/qn3HRpBpFdt7Wbs42CCVgV/0brPwahWkQ/2++YPfOb226m145dJ3X4fstrIpCF3m9UXBCd+4u5uOfNAMdG2hZBESKkBmNBeV3YH/seQTVM1ulXYK6f4dWMmzXexoyRjghkY268PyYAmHvWLOoK++HvcUzMDtAL+u9CDSTFadLsL9DoDxqEop209e8C2rZx3lGpDDnLwHxncrtNSrdmK9bWFRg/WOCp8qsOunFGnCWgnnUGZulqjG7TUPQcEFp714g98/H3Xohr7kO6EdyGt1rf23UbRtaEmJWjN34pMcFCY9hiqirqSA8ks3TC6aRk5/OLa27B1QJkX7EEVINt1TZL/llDdtDlhnv+yCPXn0t1cRGZBT04/47fc8sf3qa8roHZV53L6HmrAOiam4GqKtx/0QCWvb2bTcUeS6ogzUdZXRmjHrmVrpn5rJl4X2cw3on/CAcMyKdOnXpQXuMLFiw4ZDf0bYeiCU6ZcCxNPgXLjsSCcYBX3v2Y64F5VyxFpOgxao/IOBo0A4HwPGaF0sHiUuA6EiNnBGbFWvTkMxDJmSgZ/XBrtnrU9/xeZPzol5jvzYoXanlvJsnD7o2bmKFVtK3Fa7xVtK29wnVHsEWQzAvnU/3cdM/Op/9wMk4bTcmyMQdUaTVcl8Xn92fymi0U10UpSPOx+Pz+XkDdwWclOufuEf2Z99fP+Pv26tg1cg2lMyj/lsBtqkEN+JBEkRiAdcDjlWAmmiLo1qMbUro4IskLyNseo6ocd85V/P3h2fzj8Xn8eNRt7a6zs6SCHnlZMeXa9aUmW6ttTu/uo2cQpBQMLzT4wOfnn1v3URexSfN38jAOR0g0Tyirzbgp9HwU4fmNJ2IIOY6Lmqxy2pTvoTXupWz1tTS0jFcXzaLylftxGqpI+dVdfLbBoMcRCrWv3EP6Ceeg9bgYLTWX6NuT9xMwmo8+aALm+ub+5s8AajArlrTJnM5FP342CBmXOYrVS0ow+tyPcKrjM67dpoISSDw3aFleYO/P9OjTAyeiBXM8he9oPfauVwHp1Zg33+/+2azYwnrPaxh9L6Pr+AdBgZo3n6XuH6vRM7owYtxNrF12B0PPvYJNf/8j1cVFrLz+PK5d9TZoqXyX0ZadlZWWzIoZVxKJmnz/6F5IabeWGBRci6KlAy4IDf2UB8Ft6rDOXKgprf9X/B2+Dy0CV0mY/749vt+tvw3fSXdjVnxO5SvLcRqqyBu5iKbmTLmmCpKNcCxLjvDHzndrtsb6q/+0x3DL1rVe9/05pJ24iPI1i+JK0sralKS1PEc/nvy9OAVuocSvHfe/hw5Ze5047CCQBDNMzpreC0VRMDfd1064rWXck5FqrIo96Pl9wLW8DcI25TeKrxdGwQSwK4jsnOptOCbSXxAKGN2x9twRO7dl4xLRql3l1YW3iljiRPHZUSJWhCvvXEVe157Y1XupeXUxoYYqcn69kCX/aqKyyeK6H/cm7FcYs/IfVFRWUR2Kcssf3uWD7V5WPCvNE5HtmpvB4skjmfHyTqqaLOaedRT3vPE5VY0ms3/WjdtfuL2Tqt6J/xoHXF326NHjQG93Yj+oqoK0JVZAQ9MVoqYbC8Zb8Mq7HzP6nOFkNTTRe+hczPW3oQ8YDTKKFAZ6l6tByoSLSze6F8XfEykD6F1GYW9cidF/LMZxNyMjlUg7glVfi56ZTzRBhkQVDv6gP672z3Fc1GbRNoSXGbfC0S8VzLoWvLMmxLE/u4e0ZIEvaFC26uovrCl3HJdcQ+HRy47BduVBZbf3P0dVFeb/nxeMw8HVoXfi8IJsrEZNV5FaKpIvbrMIGgLIz/Eo5LYIoDkN7F/nUNh/CEec8FPe/v0ieh13Cj0H/yj2nmU77CqtZHDf7t7fjuT/iiL0TFXpHZS40ltMKkKQkxqkIhzi6Y/KGHdC4aH50p04ZFBVwZ4ql/ScWwhU/LY1gC2cjrl+LkRqOmQIOY6LSpiy1ftpYDw7h6yzb6Bs9VTKn5xCz9Er2fIPk6N/OhXFp4CmIt1Iwkw0SV0whs5F6CmQlEF01/5sp9swciahpPYk0iZzJPRMpLRAaCiY7TOuexbi6z0/4dwg1CDRorlxQb9Z+jhuaTPNc+AcXNvx1I3b0tT3/BXfSYs9teNoDVbRn9G7/RTznamxID3jxNm44Toa3vsToT/dySkXjQV/a/BdXVyE61hfkm/37UNbdlbQp7HwnzVMGV5I3ywDUFGzLkHLPh9kE260CKH4Pb0X31EoQuJG9yZsO+k0tP7fja/TbftaS0mDtDpQTg+XI3fcR96IKZStvY+y1TeSN24lYTWJFFGKtW4mdnObqkPnovS+GC17YKwv2EWvgGwdf5WMfmh9L0UISc6511MaqiK6ezPQviStRZMGINEQrqmi3T10yNrrxGGFRG1nDJ2N7HMB9taHY8kgoac0l07OxtGzkU4IzAaUlEyPlp73S6JF3vjs6zUPs5npI52GxM+FtL21cOZF6EZq/MZlj9leIitSgzF0Lub2Z7zzAnlIKbDXPUb2aWMo++si9sZYHQtp3PoWFU9M5b7RD/NBlco9b3zO3LOOZEuFyq0PvBy3Xu+am0Feagr/XHoTu+tNlr5byqYST6vmtpc/YeVlx6IKiaSJ+0feiqZoXjDemR3vxH+IA8qlTpw48aD+tWD58uVf+Q0fztCkwDVU3q80EQikUOmaG++T3jU3g9pQE/VhE+nLwjfsLpRgATi1SLsRxcgjWrwMX88Eqqxlj4NrgnRQFANtwDWeSiUSa8tyrI1L0IMBrKZwQmXVGlNNqJTrOC6RUIRIQ5hIKBLzlrSwCTsmFvaBFdMViNTb/G3ZLv589+dEQ+ZB15Q7jotq2fgcB9WyD2ojoO05juPGgvEWtNShd+LbAdlUg2pIpJ4G7he3f3mzZ3hmkld37uBHdROroB9/3jjScrrywm9HUV9RHHt9b4vCeqYXWGwoMwlZku/narFgvAVZaUFUK8zL2yppMjuLIg83RByLX81dwZV3/Y2P3WkofR7HyJmE+dEKZM22GEMoxWhMOI51pIGhJqXG/i80OOanWfiDLpruIFwbHLvdOKvkDUNIF2vTUqJvTwarLnFWVA+A25oxbakXtypfQpFNSKuqg6yRD1/P2/dz3JhNtHh5u6Bfzzqj9e+iWSj+ANrAa7E2LcXcsgw0DaPfpaAJpOpD+DMxjrockvIQ2Z5IplcKNZvMn15F3siF5F48m8Ijj4l7TjMLeqCo+pdrtG8hgn6NhSP6U5Dmw5VQFTLxh0qpe+Rq6ta9hJ55KmAj7Bqsype8QFvrgkIT0qruwC1lDlb5U7H/o+e1c1NBySNw5GP4ey1ERiWKEUw4v0urIea4knX6b7w514mSbIRjom7QzJjb9hh6t9Ni/dTatNQrvyh7H/CCcb3fKKxNS4m87lmr5o24Fl/3gUCrheDBIuE9vDeTZCP8X7VJJ756JGo7c/1scEz0fqNQMvp5/dHIhB7jqPrbM6iqi4jWYH3wO8wtT2IUTowF44C3WdX8/4SuEd1vQtp1KELBSOmP1VQUP74VzcY35GZ8wxYgNS+LbZxwJ74TPbZu2kmXUvbE1P1YHVNJGXwG/j5DqAs1MXnNFqoaTVRFsGpDGYsnj4yt17vmZrByxpVsefJz7KjGdS98FgvGwVtjSinRHRfVNEiWqficpM5gvBP/FQ6pf8lDDz10KC/37YMLEq8e23QlpVGNFTOujHvI7772Eh5Y8zeCfs1bkIUrQFqgpnvqvNLGbXgL6TRidJ2Ev88SjMIJmKUrkVY1rllMZMe1RHbciFCawKrHDe1D63tpc83iLIzkZE8wqI3FA8ffzuZaf0xFXQ+0r6ltgaoKimvrOP+WpQy7+necf8tSimvrOgzKbSH50dWDYr7mZlR06AF8qNGStWiLWB16Jw57SNdBNtWiKDaoKXxhATlQUVFBKGKSjEcftUUAxW1MqEOo+fycfDSzvFcAACAASURBVPkMok0NPDvzEqxIEwA7iisAyE1PxXElb+6N0DWokqG3//z0lGSikTCNUYvXdlS3e78TXx1UVRBUw6SK+oQeyAC24zGRPvhkNyNufZLyykbMdTcja7bFjpHhMtxwHalaHRmBEGmBCFrztTryLHea6gFIGjAcjTqsddcSef3XmOtuQHHrcG0LY/DUeJ/ogePiaOEyktgzXEbrQRB7T8+9lOiehc3aIHUdeo1Lcy9m6WP4+yzC33c5vt7zQM2I1VjGvq9VCloOvp63x+YQ17FR/Mnow+7A+P51mBVLCH86ksiO60FW40brMTfdD01laEdcjPGTpzGGLUL4MxFA1Ut3U/WX+6Chku69j2D+60VMf2odVz/0Cvg6tg78rkAiqIgIVlx2LKoimHZSJo3PeMr5/u5HEN11G4oQmBWeiJW5bylCkUirGunUIq1qzFJPM8DfZwlG10mgZuDrdhNG10mY++7BKlmJv8/dBI76Pf7ei7GKN0PDPmRjLW5tEfanT3glDcfNiO93g6dib3/Ku89wGWowzevT0kWlvRWV1uPM+H7a4leff4JnCdX30liNesv7LYF+WwvBg0VHfuhfJPzaiW8eHbWd0PytHuODplGyeiZVr60i89SRCOnESmHc3a8gcOI2GFuy4gBu0xbvueg6icBRj2N0m4IUOubu3xH+5HIin12PnjIQNXfUfuNZlOi6aRDajXHUSOxdL+KGihBOPf78LgSOGBJ3z3ZNCU5DFRnDR1IZ8daJd43oT1l9lKomi+ysLNb8diKvz7uRR67+DT3SMojWWziNVsI1ZlNpU8yLvROdOBQ4pD1J/q97QStgNZok6YJPK02OzPKRkZLMk7PH8c6yW1g27QoA5o4ZQffsNBRcrLJ/IYXqCbrtmgWoCD0fq/RhhNCJ7p5HdNfMZrXJOV6WnJYsyEyMQWOxP33co0fSTF2zQwg1gHbivfhO/z2NQ+7l3bo8aiKt1mYHUlGPOBaj5z0ao+/sLa9hyTOv4qcx4cLYcVzSC4L8ePL3OHvOD0gqzCVv5KKEHsCHGi015S0DZts69E4c/pDhepAuihLF1VK++ASgqqyE8oYoSp1nW+YIPwKJIRJnWzIKenLSr6ZSsv0jXlwwHiklO0u8gDwvI4UPKyxqo5LBORoksAnMSPF24LM1i79u7wzIvy6oqiAQ3kf58lHsXXgu5ctHtfNABtBUJY6JVF7XniGk5A1DVV2sf03BfP1XOOuuI0WUoqkCU02my5gHKbz+CXpMf5buN66m8OqlNG37J1pGF7J/Mbl98PLeLBRN9zzEB01o9fu2GuMWr9YnT+ErbO8Zbn3yFG5TTbOKdr5XG9lcLyydWqyqVxNkU2djlT6C2/BW84ZsEjK6ByntdsG7knIyQoC5bymRHZOwql5F0ERk11RwiokWzW6XUVf8yWhH/gprx3PQVIbiRBCK6mXV66pQU7LI+tl1lD87m/JnZuJU7yY7r4CMtNQDM6i+K5AS05Fsr7GxHZc+GXosA6cF02L+yHpWq4iVQI1rT2lVNwu4zUNoWVi7XiW6/k4UvSu+rtPRU85GujrRf9yIuWU1etaA+Cz2USORTgTpz8Z38hL8pz+BfsykmKAWtNqW5f16PlV/vhdQ2mfUjcTCf1K66MfdjJJ6BMZxMzCGzkXJ6Bd73yg8ktyxKzGDXQnQdMCNsrboyA/9q9ik78ShRUdt18LIUILdkP58sn5+PXmXzACzHKSDPmhCrO+AEjdGeVnx6bHXpFWN0LKI7pkHbhizufzGe69FyO0nsfHM2+xywJ+B0Pzg2uh9zveelbeuI7puClmnXhxjdEDzJmuoBqHqZOXk8PBlx5Dq1+iaHuDBS46lMipINpJIVwOkGX6EIvjB5f0ofbuYu84+Om6NedfZR7Pjxc95a9lmNPk/MPZ14mvBIR0ND0YA7rsMW0j8usIJBT62VkbYW1rOuPleYNs1N4MVM64kPT2d4roomREoNGz0Xmdi7nsAX+HVSKsUVzoxK5SWXUPFKEQC0qrAbdoS+zxplSLtEDJSjfBnYwydi130CkIL4EqXutoIqs/H9kiAmkhrgPpFKuotGacWHHdkd24fMZiK5aM7FGkTiiDqNtN5LVADBeSOXRkTcEmksv5loaoKpqK0qzfPNRRWXz6YiGl3qqx/yyCbalAMgUAi1eBBnVNVWkJaug6REKrZhK16Gz06jUQ78L3vOmAox51zFR/8aSUp2fnsUI+jICsdRVF4Y0+EvCSFPL8kUUCeFkwGoHuSy7/LmyhpiNIlxdfuuE4cWgRoonz1lC/Uokg2DP7621HU1tVRUtPIax/u5dhz5uJsaLUq0/uPxfzX9IRCl05dNVWvLyf79MuwNrT6cmeeOofGIWMQips4eNH9qD0uwGr24BWBPHzDFsT58MqabVh73sN3xN0I6eCG9mF+tBIiNSB8SCEwuk5CNFOVW3x2W4Q7PQX2dISeiVn2ZGz898b+Ssx9S/H1uAWj152Yn7exySy8hsiOG+OUjWMCcc3Bf9z3sUpBESh6MlqfC0BLAsUAFBQjBd2fRcHlv8WqLSNwxBCC3zuzneq2s59o53cNbQVQd9S55GYYMXcRO1TXXAdbHROxApA4HbRnFq5U0bv+EKv2E3Ak5uYV6EdfgRSGZxnVZVi8NWob+zrwSrXt4jfRe52LjHgbhZ7V3hwcmULN6yto2vImzrnTvDrb9a3Pg/ClxfXTlnPtxhCaz4iJFbZV0JaRahx0wgQIhPbGns2ORFvbIpEfuj7kdhrMAAfDiurEl4OqCgI0obhWhy43B4uEXvbNfUIE8jDLdrFv+XUUTlyJ4kYwNy5pr77uum0s/ko91ogSwH/EPSBtJAKJ1vz8dDA+Sad9kN5vEtKOIAJaO0aHuWEOBb9ZjFmxj5p//J60Yb+k7u1nMH4xlXBUcs0zG2PCwAtH9Kd/doAPH9vCvo2VJGf6+eHYAdhOLQN+norUG/nd2UfiIAibDv6ARmqXJHbtqo/TTFBVQYQwlmujKxp+At/pMbEThxad25OHEI7j4iLQIxbddIsL74jPMo+Z9yh3XHclj20o45bhuSA0hAJCSwUEQs/HLn8SLedib5HWLArjOo2gBLDr1sd9Xgv10Rg8Hdesw9q0FGPoHFwpKN6xhUWX/6TZluaPkNyHqCMZGAyTotrISBhLTTxIt2ScWu79lotOxnzhti9cGMf/FnK/9/77YLzcdGO2M/urqecEDSoqouB0Wp9+m+A2VKD6PKKOFAfHoKguK6Vr3gDAQq3ai5OXDYBOCMju8Lx+w0cQrqvi3eceIFowjO7HXcKmCpOKsMvPenYcYLcE5Omqp/7+dlEdFw7M7fD4ThwadFTb3ZbmqqoCo01w0H3AcKaOuAFUHe2kJTiN1dg1ZbhNDQmDahWLktVTyB1xI/bmu+ID9g2zCJ54NwInYfAiXAuZVIAxbDECGzdSg7Rr8f1wAW5oH9bWxz3RoZ5nEf3HjQh/JlrfSzH6j0PaEbCbsDatwDfkJlwrEhNii7loZJ0RC8Zdx0ZLOxEZ3YXbtCUWvHv1lL/F1+ce/H2WAA40Z2ziRJLaLHI7ElFyG0ux3l+EMXSO54fdNigbMhvz8z8iK/9N1qmzKX9peYeq299VtBVAFUJQ1igIXDyf8B+mU/3qKrr8Zg5m2WqMwon4es0DNR1JACP/cszSx+Pa06z9F07pvd7myaC5uK7mlTt8/jJGr3NQBkwEfw76seNjSvnWJ08ha7Z5GUFA+DIx+l6KG63F+N4UUH0IXwZmTS2Vf7qJ6O7NXqmYFJhaAUknLfY2kqwGzE+fSuA/fjtCT8V8+/r2CtrHTAJfFg1m4KA2yjRVICPVZPijMUX1Bhnvh97QqbL+lUC6rscs+hIbJgdCi5d9cNi9qESbxzZvg8YYMhurLoSv+0D0tEzMf01LqL5u73wRrfeZcetaFD/m9rVg1qP1H40QLkav+SDA3+c+j1lS/hRu0xaUlJNBqPiPeABpV7W+HizEDTcihYY+eBpC9SH0ZFw7ApEKXCuEnqySc+5Eat/6ExmnjSakp7P89e3ccWou2X6ojMDKt3cxaWh39m2sBMCXqlFX9RmPz7ggZod2wbznWPSRwqaSEAVpPpZfOJCKT2oRqmfTq6qC4qYSrnrkFvZWl9I1M59HrvotBUldOoPyThwUDmlA/r9OWVdVxVsPudDUEGmnsL63vIYuqTqLhwdofOYaqo4YQtbZ12DkXkp034P4es7GLH0C3ChCTUVoaQgpca1yrLInMPIvR5p7cRveigm+yMZGz/fxuJswTlqEtfNPGL3P4/lFt9LjmBP4xTW34MNkcFoTNNZStvJG6r5gkParOg/PuDJGW++SkYx1kCJtXxXaesBCp5r6dwWyoRytOSB3laSECr1tEW4M0RRqwJeZizTqUco/w8rvBoAuEwu7tUAIwXG/GI1EsO3NtWBYvJHTj0y/n8IkGQtk9kdywI+mqjQ1NpCfksM7nQH514KW2u62QXmLFkVLBkiVJlZDBWpKFmpKlpfl/teUNoHkHGrffZH0E85JGFSD4gm4BdOwE9VJYmN+vLxd8GIMnooUGqajUN1goElJbla4ubyoOUv9/TlACtKsb/Ulb7FDA0/dPFLj1QbvfgXt2PEYBeNA6BgFY7x5QAkQ3fdg65jfbaqXac0ZgVm60rtPqxThmri1RYjUPhAuRyRnxwXdbYPwFhGleLu16UhTxmqJ9WMmtcvM+oYtwAwVYW6YTfoJ42j6+M3Yd/m654NvCo7j4oQiOLrG1c9sJjtJ575Ry6mursZRsjEKrwenFqvyJc/Srmg2SvA4jIIxCKGBUDHLX0CGN3rifGoK0q5GGOmY6+/wFKsLTkYv+CHIaqIlS1vb6NipWNvWeps5gLSLMd+9qTUT+fGDHluuz7WxYDxv5CKiSjLJdglutAqrOXsJYIWK0I+ZhJLSDVvqNDlJBGnAOG6Gp7q+/amYgrYS7EadmYrtSBRx4I2yFlXuyD9aM6opQ26nQebvZ7v61a4XD2WW+NsEp7HmoJhFbWEYCgG3AVwbFI2wkoLZRqDMdiS1YT+aGiAY9GEcd5MXmG+6FxmpJm/EFFC0OAeHlv6jpPRCA+zd/8To+RPAQaDiRqLohadiffI4wi7HLHsCI/8Kop+3GUO734RV+w565qlEPpsUZwFpVqxFIkALYO14Hj3/B1ibl3obBYOnYhW9gn7Ur7F2PIfW8xwyTrmE8kgSToPF9MEK0Wevw64pITujC9MvmofVpoD3yFPSeHzGGVQXFwGek8TzMy5k3IK/MrEk5Im6qR6l3TUdpN+iwY5QVl9Fbkome6tL2VtdylWP3MILE5eiE29n2IlOJMIhrSE//vjjD+XlvnXQpOCD57cjBCSn+BMqrBf4nZgQjFm2E7CRdi1uw1tYlX/C13UiZtEspF1FZMeNhD/5NdGdN+A2vOVRdArG4u9zXzPVJw1768PISDVuaB+yYQ96t9NwFT/5fQYw8f5n6N6nB0HRiKzeR9njN7YbpAM0xd2jqipoUqFHegZrfzuRdQ/dTHZmxtcm0tYR2nrAtqBTTf3bD7ehAtXfnCFXvjhDXl3uLSZTM9JxM/KhugTX8gSXdLe+w/OsRkHVVpWaTzWyj7+Uj7KGI4s2kPfCeL4fqO4wGAcvkE9PTaaipo6jc5LYXNZIY6fa+leOMEnkjryrnRaFqSbHasv3LDiPyhfmk3nGeLLPGt8uy22+N4uc86eh5PX3rMjaCGHpQ27HcvTm2sK6xDWujcW4Zeuwtq70asVPvhffSYuxdqwBBDurGrn5tR1UUx8LxqFV2ZxoecwOaP9rSzvi3VPqERjfm4i58wZkdBfRHZOIfHIF0iojsnNqTLCtxfrMKBiDWboyRl/3rLEcz7bKCmF99izmtifx9Wh16rCqXsXXc66XCW/aglmxFn/vBfiPeKBZNHQFQlVjv1tLFrYFMlyGjNbFxEO1jPjv83XPB980bOnNPxtLQmwoc7ls0VpsO4K09hHdNTOujtyp+TORT64gsuNGAGR4I0b+qNZ62L1LwK1BIr1+YdYiFJto0az4/rRvIcagcZ63vZGG/WmznkxzJjLWNvl96DHzJbpNfAg9LZWg1oj13sxm3/lWEUIZqUaoPmz8NJoBktwSr1Z9y3JvU2jwDHwnLkTJG4aNHstmdySC2NL+h4Oi+sHqTxwsNFWQFoiQ4Y8XhDwcIe2Dd7nRVEF6UpSgqMGt3kn507dSsmwMvobdGEb78MB2JBIwNz8E0kLvPxZ90AScqvfADsXrHfQbhZI3DBQVoaegpR2J21SLW/05sqkWYSSDno7xvYlEi2aj55zXfgzdfSdG7nmxcpvY63sW4iu8BlwVtABGz7NAgNZvdOvz0ONMzPfmoPU4s3k8c6Cqiiy9nuizM+LWwtFnZ5DRpn/qAWLBeAuqi4vIMLxnoCDNh64pfPjCDt64fyO7ivdx4h2XcNNzdzP9rDEc16M/AHurS7Hd7/5GZScODQ56Bn3zzTfZsGEDdXV1pKWlcfzxxzN8+PC4Y1asWHHIb/BbBRf2bazCSP6cAWf2iMsyd83NYPn0KzGEExsIsk7/DUQrkXiKuk7Nn5E5Fxywzk8gCe+4lsDRv8d8+0aM79+K1JKwP7rLq5Ncfxu+YXdx0fT52BuX4Jati2WKAkcMoeG9P8Wu157+qSAbHf6+bDON1RGSM/2cPG4gVsBP7si72lOgSOLrqv9q6wHbgpiaemds9K2FbChHDQaQajJSfvH+YG2lJ8aWkpaK69dRS7ajV+3ByQqgyYZEJeCEihVK39ObLXYFH5smn6V/n7SjT6Tb+nvh8auIXn4PvvwjOvzcjJQgFTX1/CQ7iTd21vJxeSNDu6Z2eHwn/ns4jiQcKGynRRFwGttlgCqeu52u19xPNJEvuHSpt4JoalI7yqyCoHD8Q7jRRtScWZhtasiNoXOxNi4BwK3ZGstu+09dhdrjAkofm05qQxULL5qHSAZZnWC8TsrFdbR2NbzG0NlIq8mzn+pzIQSzEHomiq97qzVQh3OAirSaa4ZbROI2P+yJGm17LKagbQG+I+chVA2EhrlnHUbWBJTU7rhWCdE98+KD+qi3oSUCeUiUmJd6i0e1NGtjmwtKUgZJ/YfTtOXNb2Q++CbhbVoTm49WbShj0aSRXmkDHLhOX0qMbtPBrsconBCj3kZ3zcLoNwnUNIQbBdzE56sqwsjA2jA7JuIGxHyglbxhCKs+rq/5hi2MMTRaNpaEnoLwZ+E6Lo1Rwwui1830fOr7jYpngwydQ6OTRAt9qWWjrHz1FNSULDJOG42e3R1XeIGwIu2EbJNDyaDQVEGyEY49y4370d8PVn/iYK4vhYZwwljrpn8rPNSFZnTILGoLTRWkqNXQUAKaHy1ZJe+iaZQ9u4Cavz1M7vlTkX7Z7vdVhETtc358HxkyC2vbY+3o6r5hC3DNEObbk2N9ySp6Bcwa9AHjkFYYkd4VaZWi6DlfWDse97rrepsPW1e2rnOHzkbJ6Idbs9Ubu8JlnoCh1YhAYfm1p3D1oqcSblhoqkvBMVkUb6zCsRUyC3rEBeWZBT2oMb116KIR/VFrolTt8sbMVN3boNpbXcqNT89nznkTGfXIrXTNzEdTtM41aicOCl8YkJumydixY/nwww8ZOHAgubm57Nixg9WrV3PssceyYsUKDKNjC63/JQhVkJzp5/N3SknO8pM3LJ+7p4wmN6ihKiqNjoJUwrHBUg2mISO1WI2vxig4QkmK1QcmqvNrVatUEP5MzPfvwBgyGxmpjqleymgNipHqLdDMGm8x+d4sMk+fj1m2k+juzUD7QVqTIhaMAzRWR3hr2WZ+PPl7CRfGXyf9q0VNffKaLXE15Ibrdo5132K4DeVoWRpST0cexE5ya0CeDoqC9AdRyj7DzkpCd+racX7CVYKS9Tq+NEnG0Q04pqT8nQYAPu/6EwrzcpF/vZ3yZ26i8OpVKL7khJ+bnhpk647d9Mz0owj4sCTUGZB/DXAcSVhNIiA86mlAafIWhClZZJ19A2pSKk5TPbVvPoaUIiEt3VV8pAUi3tiFRoOVgu1INBWS3ZJYNs/NG4Zv2F2YEYli+JCaGhPLans9FIOat1+MjaM8O4PM8csSjtdSgvX29V6gc8wklGAhMlKJtfHeWEBl1u/A+NE96AXXIVvOs0o7nAOk2YivxzywG5CRWsyPViJrtmHW7fCCLSMdAHf3K0R3vxILytyP78cERMbRGMeO2i+on4750Ypm5sBshBCYHyyNqyG39v4dLWcw2sApWKEQGaePJfPc6bgu/zN0YPDmyY3Pfsr8M49k+iufsqkkxLNbDI7vlYu0Igeev4VAWnVYJQ95zindpsbYDkqwENlUg/nBfK92PNH5zWWBMlKNktEPre+lXuBhR7xNlIHXEF03JS4wckP7Ys9Fy8aSCOThO2kJjQSRSFQs7HAZ+qAJ7cWx1s/CP+xeos2025aNsrxrVkG0Hqd6H059Ba4ZJimzELfZJ73dc3iIGBQtlHhr3cwOA+SD0Z/4Mtc3Bk9H+DNbNzaaBSHjKfiHB9TkjINKoCT7TERT9X4ibNPJHjENlTDmulYNiba/r0C27yPvzfH6Ttm62PU9vYJGiFS2Hrd+FvqgCQCY79/pjYtDZ3p9XegdjKF24mdBMbB3v4zb/Jkt/uj6oAlYm5YirYaYgKEUGhV7dlNdXERDbSV6gg2L7dUmeb/oxcCf98Q1Ha5a/AKPTD4vVkN+1T1/RMvryoqCQpJMl/f+sN37HTP9VDa1lqfurS4lPSk1VkPuJ4DzP7BR2Yn/Hl+Yklq1ahW1tbX85f/ZO+/AOKpz7f/O1C1aSasuuds0G9tgAwZsQj5Ig1ySUJMAgRQCTigJGIxNdSEBN0rozSH0S6gh1BvgJgRMh4BtTLNxt3rb1e5OPd8fI4201rpwMYSAnr+k2dmdduac8573eZ/nySe56667uOKKK7j77rt58sknSaVS/OlPf/ocTvM/A4oi+NrUwI87WZdA+qCbEc54ZCWH37aU6X95jw4ZJXLk74Jg2PEQRiJQWFcS6LUnISWYQ6YXtr0ZMgPfbcMcNB3pypCiJrRo6EPaQ3kDEdLYoMeHt53kN34JbMGKzCcMxnvQ1ZoDv1ukzYvS6SdIe5+PcqSqKpiKiomKJgU1UZXbjhvP41P35rbjxoeCbgP4z4VMNaHq4GuldKewt4q25kbixcVougZC4JfVQdtGXBlB9dry7Pw8B+pfM9CjkvLRKcBFNTxajTYMPUpMqAxNRin52k+xmteRfumeLR63NBEnnc0hPY8hJSZLN229Xn0AOwaqKohlN+ZRT2W2nYrvT6flsSvYeNNUWh67gvJDTsdX4/1p6fvOR3Fa8Zb8Bvu54/GX/YESo5NkNE2x2YX7/p8QkTKMSXPRdzoGP72eTCe8fM9GhHQx91+Ase9lKMnRwe/teR5W4yZiu30tPEe3bRO61IKa4Dybsktw3rm+NxB65TysJdORTjrI4CR3wzhgEeaBCxDCRVHjOM1PYg4PqOabWwOFtd7CQ9oW1t9/i/3yrNBvXWYbEJEKRKQC84CrA8uq6skYE2aAUHupym3vYb+9GKPyTKK73o05fCHS0TDGTA0CeieN/eqsfjXkxojv4+Y0mp+5B9WIIXNppFA/t/HgCwMfNr7TwsYHVnLl10byyIkTOPvgUdzwUifotZhDz9/C+D0d30khFAN90NkIvSzwnq86tjvA0LHfuCwI+N6/F3PY7H52efbbNyCkhz7lKoyJ53dn/1K4TW+hRMoB+okXuh/cgTFpdr5v+T6zaXhwAWZ6AzG3CadhdRC8dGcV+0JmG1B9azOr0+6FgVQLzY/MZ+NNU2l+ZD4y1YIvNfR9LulXHtJl7xjBv+2hxG+LVv9Jf99+a344l+rZ9kXVTBCKEiZQBp/7KFWnLC6oFaRhh17h0HudRklZf4HLvvdXFnad6FkIDM8jWo3Q47gf3pu/n54I25nftgJn9d8wRsxHSok5fLM2P3wuTuN9BSwg5yBdH61ibN4xe87DmDAdd81TGJPm4js50KLc+7vfAPDoDb+n6Pvn5ZVCmcfMY8GLrUx7ZAVtXS6+C+vf0Dnhkqe56PFVnHTFs5TV7ELchRIES/+6ipbVncTLIow/cTjzn+1lBw8uq2FIWQ2PnH7dgKDbAD4Rttk7Pf3001xwwQXU1uZ3brW1tcycOZN58+ZxyimnfGYn+J8E3/ExEzp7/3gXimtjpFWFix9/P0+IbOHf13HGfjW4Ry5CrRBYa4PaGHPU1Qi3C/SKUGFXKnHMkfPAyyH0UnwvC5k2nI8eRh9xRG9tomritCxH2+VElHgd0mpBKlrY+UHQOXqdrehlw6n79a2oiQqyWhKvj3AHSrDa1zcoj5dFgmWbzznu3RJ93oirgYjbgJr6fzyklPipRhQ1irudHuTtzU2Ullfgd2sH+GWDUDd+gN9locVTqMLD66a+t67QcLNQs3cWv8/kaXU6i4wXs0+nT9SPIWp3JjZyb5qfv5vEpB8iYiX9jpssDizZWjs6GVEW5Z+rO7BcH1PboTIcA9gMMZGh4c587YuGO86h4vAZ+XTU++dQPXUxXbI2j5YuAXdJYHemJEejjzoSa8nZvRmhiTORioHz+txwW8k+c5l0VK/adE/Jj9ORov7+Kyg/9AzUWC87Irb719H9dpyld2AMPw0RKQWzHDwNv35J3vX0TBhFcjf0CacjsMitmh5QNhMHYNadghRGrxqx9DGGnIMQJmgJ7HULgszqsHmFld/1RL46+qS5SLMM6XTl0ebJtSEw8FONuCsfRO9DQTUPuLrghBskbc/fQ+mUY2m493zURDlVh08jGbXx6U8b/tKie5xsXd1J6w1LmfjrcZz6z1Vs7LCYOml3dBWMupORnt09fmeQbks4rlsbrsMcdTl6zS+xVk1DqKWYsJYfxQAAIABJREFUg6bjZ9N5dnnSFhjlp4Uq6z1MCLnzcQjPxuoOppTqyei7noC15OxgQWWzdhGw5zK9gltuDifrkFn+D+yNH1Bx+Azanr2V6iPO6baQ6t+unKa1xMoMUn3o3opns+n+Of3ew9pTbiZFDaUHXofnBCrrrqcQczrw1U8vrrY9lPi+tPpPWma3pd/vmUvBF99DfbtcbrYQWCO9LTpSFAsbUAv3PWZZuL2n77E/fjyvtKLHv1yo0V7Wxvu3Qd1+oGaR0iYycmFwvoqBbzfip9/EtlZ3WwYmkH4Ome1CMYug0CJAtALfTmOM/TW+Z0N2AzKxM51NQYZ9zTuvcPvvp3Hkb+ZSM2I077W6XPXPNt7pXmSPVER4+7YVtKzupPH9CN84cwLZ1jTSBwsPQ1fY84ej2POoUaBCp0zRkG4BCLPiMYrwPDmQGR/AJ8I2Z5Nr1qxh3LhxBT8bP348a9asKfjZVxFCEeBLjLjOBtujPmX1EyL73w9biSeKGTYkibO2V6hC0ZLYTQ8CWqCgu+E6rI9+hbVqJvhdSClwXluI88ZC9FHH4Kx6GJCB+u8716DX7Ifb9Ba5536K/eZ8cNIoI38Y0na0sefQ/spfcTsa2HjDL9l0y68xvK68c3OF5ICpY4MgHMIg2BU7plNRVQVP17BUFU/XAlX6LUCTghcK0Oe1rYhvDeA/CzLbgaI4CCGRSmGq+OZob2qipKy8d0Mkjown8VoC+q1JQEe3OgTtK1WKh7oIvXeByfMljekskWicPXI5SloqQEJ8j+/gOzmy7zxZ8Lg9AXlLeycjk1E8X/JBc6bgvgPYcdgi9dSI9tsmfBfXk3RkI7TliujIRhDSDTPgxoRzkZ6DiJQB3RmhN+dBtwp6uO21i9G8xn7icODhpVrw7Sy+HWSLtGQtVYdPw35tFn79EuyXZwWZ6+fPRgi/oJibMMvQR58IsjP05VViY9Crj8O3NyLddoRWirV2HtaqadjrFoG0sdctwM+8210/mcOYNCc/6zlpLr6bRR93GkpydKjqrrhduP+ajzRKMadcQeTg2zH3n4ez7hncFbeijzoaZ+VDoWidiFQUFrhLr6fyB2fS8tS1AFR88zjsl8/BfvZ4vCW/ISHqv9BiVzsKfcfJ8uFFDKmT/Pnowfzj5F1B8bA/noG9bj7C78RaNZPcR6dib7gOo/IInMZ7gxpzKRF6bXdmPBF402fr8+97rgnn7eux/nFWyIToyTr2zWwG4lWzg3atxftnwyfNwV35IParF2O/OQ+hmmAH85Ked8lau4yGh6/BzWkY+2z2/b1m0fnWMyheLl8UTXoF302kH1CbI2V0Ogm8jlYabvjZDhFXA/CFVrh99gmQ++pPbC1L/El+v0fdfkdn/P9d8NALXidK4et3GlazfuEPcDrb0caek9dGtD1m4EkTfd8FmP/vNozJi3BWPYxeM6lfHyWKRyHideh7z+r9zOhe7PCDxSvf3gi+i7PpRswh05FOK9bqi7DWzkNoSdyPHgrE4roXAXp/fw4+KmTrsd+8FPvvJ+EsvQ4BzLjzH5x169MMG78vqeZ6RHEV9ZRy5SvtYTBeV2KidjeReFmE/U4czYt/XM6bD3yI3eVi6Arr0hv51rW/YPTvv8u3rv0FHdk0j/32Rl6+8L6BrPgAPhW2ucQnpSQSKVwns6XtX1koQYlXyvc56+H3mP6NUQWFyBwf2rpyFOcJVfhBVtxeE1im9KwGeinspocxB52KPn4aZOtxProffdTRSL0Ed+X9+A1LsDtXYk65Ell3AM7yGwJxtwOuwu3qgGFTaX4myGxgRDCHjsVau6wf5crzfNS4ykFn7RlkxJVg8rEjaOHb8hHvh63Q5wfwBYF0UOwVgIpv7AZC/WRfTzWiRbsV1tXibepBea5LZ1sLYyZOzNvul9Xhta2AoUWYsp0MJTQv11B0KKrL0FeI/5VWFyl9ditVSclGqjuHYGZikKzDrBlF66sPEd33RwiRP2EsTQQLBo1t7ew7bCiCoI58XE3RJ7rmAWwbfT2MhVCJ7f71PIstLVkbBsR9t0lFAz9fkEmoKtrYM3D6irVNmI6zYnFo6VRIUbzQNrNiELW/vB4UjVZPZ9C5j6IIkE5XwYyS70uMCTPC4KmnRtPubMcoG4J0m8IFWb3mlwjfwlp/FdKpRy3/MZFRVwIS31rbT1Vd6KXYqx8Jsp6RCoSewF52Q6+4Ud9rtDvRxp6OsNuwuqno4T4rH0KaJegjj+j2sS7DV4zAd/y12fmiTUuvwZgwEwgESQvSWr+gdbU7Ej3j5DenT0BNr6PhlpPCLGzdGddjO/VIpx67fnGQIXdTSK89fIYB9VaCUANBvvf/jL7zcQizLLCWW3YTfsOSgHLb7znMxndz+dlJPYGomIA+4vvYr83J0ysAsNf/L9rw76HsPhWZbcRZcQvKqN7StZ53yVq7jA03TCW2+9epPOxS/Gw7akktbf+4j8SE74KiUCTb8VSDLDF8kS8eZg4dG5TEqSolShdOW5oiIWh55ub/s7haIXTZURL7XBLSynsC5JQdpe8gsl1Z4u39/UmX4OtlGAff/aXxUE/bURKTLsF5Nf86004Rsc2uXxt3Lg0P/QEAt3UTbW8+RvLAszDKglILzwGvcTUtT12Dl2ph0Om3otV9HanFA3tH6QESe9mNvX3UXhdgTLkcEKCo/SiPUgik04pdvzg/O56z0Hc5AR8Nt+nVXpHCaBW+UBHSxV7aV/9iDo0PLSCzPBCg/PX8P7HJL+KqV9s4ck+bqVOGcvr9y6krMZl/yC588NBH7HPsrriWy1sPraRldSflw4vJdlgUR2JkOnNUF5X3szaLyqJu5uZ/drsYwL8P2wzIHcfhwQcf3KLHuOtuXx3Nxx9/zMyZM2lvb6e0tJT58+czfPjwvH2uueYa7rnnHqqqAo/fiRMnMmvWLACy2SznnXcey5cvR1VVZsyYwUEHHbRdx/68YEufmKZilAZB+B9fXsfs7+7Cva9v4PvjakjGdSriBve/uYGDR2mU6DUIvQy96thAyVMtxWl9EqPmBKzVs3o9F4fPwW56FL3su+Am0IYdgrPiFmSuNRDSWPdUKDZivzkPY+LMwLrE90DqoCdITDyMlqeuxUu1UH7YNFoeu6Ig5crz+oik7cDg11EL+4j/6fjxKIUC8i8QfX4A/aHYH2C0zUfxGgHwteFYZecjtdptfLMXfp+A3FOKtlmD0NHagpSSRGk+Tc0vq8PZGAQrhmwj0ziCTINK2a4WPr39k5TwVEPw/65Ri5RvUZ6uIdFehhXPEBk5iY4l9yJbVyPKR+Qdo8eLvKm1g5iuUpMweGdTCibUbPf1DmDbKORhXHXYbBohnFBV/XAOoigZBgNaspbqn8wHTUeT2xZkst9aGPSb3eJWPZmvHmxpm92whg03/yYIWA6fBko3w8frpV/23d9tXocaK0IffyZCiyDdHL40aH54AbU/nYv0c6FYkaJX9lLXY2PQS/Yht/IshF6GUXNyvgDbsDnYb18LVhtUjEXo8X5CXj3X6Cy9Duk7KJFirJdm9FdBnnIlvtUeTGbTa3DWPIE+5Ns4Kx8IBeKEmcT+4O7AXrNrI+Xf/NkWfdu/qHW1Oxqe56PQ1a+cwm1a171g0j2u+xKhmNhrr8sfz5sfx6g6HHvda+iDv7mZqvlcGHcaMrUW5+O/5FHNiZRBtiWvvUnpY+xyLNaSc3vbeLc/uT7+TPTy3XFWLA7nCzLXipfuCN8ldCPvXSqZ/CPq752Dl2qh9pfXERk2nsb751B1zGw23jw1pH9bRYOpPuFyGu48OxBZPOR02l+4l5IpP6LpgUvC36s8+iK8dEsogvhpPetdT5JSa/o5JuyoAHmLv5+RgN69139+0OV6khSF7qOP1+f6hRBsun1m+Pza/3E75d8/BxDYTY00dpct9LSn1JuPQaYh9LzvEZW0lkzP73/e+D3GxBmBdkzxYISfDRckhV6DMWw25vBLgsz46ovCmnLUBNLN4r5zFcbuJ5N77udBW+/pr4zigCVUNBwpNBofWhgu6Lptm2i8azpVpyzmuQ/bOH7SEGoSJg8duwdOymb1AytpXd1Jx/ouJh69My2rOxmxfw27f3sYVtqhfUMX2Zd9rvqvC7jhjXv4yd7fZ2hJHbqngSpw8Ad0jQbwf8Y2A/I99tiDRx55ZIufjx8/frsONGvWLI477jh+8IMf8Je//IWLL76YO+64o99+hx9+ODNmzOi3ffHixRQVFfG3v/2N1atXc/zxx/M///M/xOPbR3X9POB5Pi4KdnuQAV66McUTyxv45eShTH9kRZgZXnT4biQVC6NqPsJrxVo3H6GXYQ45D630QOz6O/Mz5PV3opd/B+w2rBfPyjumMAP6ZU9tTk9Qro8/E4TAS7ey8aaped9Ri5Kfu02N67MFH3EopNHfQwt8YbMa8h1Fnx/A/x3CWYPZciEoCdzkVHA7UdOPEmm5iGzlFaBsn/q47GxCj6lIEUFKA3C2un97cxD8F5Vs9vu6iR8rw3cdDL+V5mUaWlRilmfydOLetQzqO1sQQlCsKrjSozPaSrKrCtXRiAwdR8eS/yb77t+Jfy0/IBdCUJqI09jWDsDwZIR/bUrjS4kivvw03c8LPfZLeRO312dTedilOAccFyiq//NuSg78SZ7Ketuziyn/3jnEDbf/99+aHwbgPdt69DeMfebgu35+7ePEmUjFzNvWkyEyh46l4pvH4bx8Tl4GRtvzItx/XdK7/9hzaHj4GoDuANZALRlG00MLsdYuo+35hyk96AeYQ8/HWnspfW2u9KpjQw/rINN6C8bgM1H0Wvz0JhBlYLWhjz4JZ+VDGLufXDBDrxQNRd/39wgnhexDy++7j7RacV6/JO/+OOk1GHtdhMw1I8ySIBhvfivMuqs7T8XrbP1MlbS/6FBVJag13oyy3fL4zdT8Yj7CDcZ1Y9Bp2C1PFx7PpYcx/Hv9FlOc927HGHd6YEVVOxn3w3vDOlzzgKsRqokx5Upkag3uB3cghEBaHYWFtrRIMB/oWYAySgM7v7QTLMw/eTUAtScFZQhOyzpan74eL9VC1TGzcNobUGPFwXWKIAte+vWfgpMl6qfIFgW0cBWPTTefQvlh08JgHHotCcsPm0bDndOBHeNZ31Oa0udqP9Xvfd6//0XBlq6zZ7uqioAVkWoJ97DWLgPbwnOyND8yP5/98OdZDDrlapzXz89r09JqL9w+I5VIqw0hfHLd5TsQ2JrZa2ZjjrqayKjLu9t3C1AKXetw3/tj8E7sPrWXwfP+XWjDDgGh4Cy9DmPylXiWS8n+P8RL5S8IqdKlrsQka3voAp5f8EbeuXW15iiuiTFi/xp2+dpg/veat8N56L4/2Y2Ny5s5//9NxUn7/POa3jnq16aORY2rOywod10XS83g+i6aolGkFuHYAwH/lxXb7BXvvPPOrX7u+9tuHC0tLbz77rvcdtttABx22GFccskltLa2UlZWtl0n+uSTTzJv3jwAhg8fztixY3n++ec59NBDt+v7nxekJ0m938aVR4zhrIff5es7VYTBOEBFTKfWbyaabcPzFbz2Bb0Tr8Z7MaqPw0+9gJV6IciUVB2LUXU0QqvAz/S34BF6UR5FEbonY/E6nFQ7alFZSFGHYDBUS2pIy6LPtc5FExT2Ed9CLPNZ0ucH8Ckgbcy2eSAiOMnT8KUOBniJUozOmzE7bsRKntv/a1LSYEGTBanuuHtIUwMjYiq+WYWU25boa+u2PCsu7S+6JkuqcLNrEbIJq0OhclwW2adv8iU82VmEkmunuigS2gd1Rlsp66om3lmCV+5iVo+gc8XzxL/2837HKCmK09wW+I4OLjF5aW0nDWmb2oS57fs2gO3ClgSVfCvFxpuC7FzNCQtpeiQYC0q//lPUWDGJvQ4DTUf1c9slyIRRhjH5ChofWoSXaqHmuMsQfhpptwfsIggy29EaUA023X4e1tpl1P1iUT+qtv3aLJTR02HYVLSiEpR4OW6qjarDf4uX7qDlmT8FAc6PL6F40hEUH3Im77W6RNwEUSOOMfjMgCHVnS3f3MPaz7yL9fFMIsP+gL3kfCLfuBNtlxNxGl5F3+PXIMCYfCnOijtCxXURrcbv2oQwS3E+uh9t2CGFhZiM4oAm/UEfJeRcKwgQZil+rg196HeQtZPDLKuaqEJGa9Cq873VC9GGv4zoERztstx+fs9eqgUhIqE2gFAT4XjeAyU2BsUcETDYNAMiSeh+LqHwYF9hvu6xXeZag/a59DrMyQuQgLbnuUgtDh3vFXy++G7eAhR6CZ4DDXdNyzvvlieuJnno6QCUH3pGQGM3InQ+dxuJvQ4LSkI8l7LvnJqX/a46YRHZ6CCKZHsQ6PQE730QWLwmgc3dXT7/dqKqgiiZgE6vql8pu74e9NwDxXfwla2L7EXJ0PLYlVQefVHec1eLKyHVVPBZC03tH3zb7YUX8NLrsV85D/M7d+ctWjmN9wblHTJHbtW5GOWn4bx9feBr7mUDl4poNag65pQrsVc9Etj8rnwIfaeju2nqC/NYVS1PXo21dlm4IDT3v3YlaaoUK4LvnDUKIyLJpiRvPdZMrtMh3Zxl928PC4NxCAL1V+56j4NO34NUc5bX//uD8LNIsUG2w6Y4EkNT1E89XzUjCuvbG2jqbKU53cafX32Ks77zU4YWDx4Iyr+k+FTLlLZts8cee7BixYqt7rdp0yaqq6tR1aDGVFVVqqqq2LRpU7+A/PHHH+eFF16gsrKSM844gwkTJgCwceNGBg0aFO5XW1tLfX09nwTl5Z9tvWdlZYKuthwj9qrmzYc/4ppv7oxZEc0LQs//WgVmei2Nj8yn9mdz8yZeXtsT+MmDQ8qbUXNSmCnpsdFRdvoh/kd/DgdqqRjoE8/HeffGcBW9R5hDanE6X3mY8kNOD+nqNT+9HL2kijJlx6tDV1ZuWSm7qyPHgkN35dwn3w+ZAgsO3ZUiIYhv5Xs76vifNT6LY3/W7XVzbM812PW34bjr0QdNJxrpm6neBVf9Fm7bU5TVHYNa1MucSVker27I0mn5FBkKg4qVYDKZ3oRepbIpMoZYUSVJ2Z53rKKi/EA309GKqmmUVyTZvIJGVg/C61yNoTcRK5OU1EiQvSv/b6Y0Njg6IttJXUUcVVNQNQWQ5Mw08VQpzqAUsSGjaXvjSRKGhzRieccoTyZY/tEakqUxRvsCljaxMeczfuS/p939O9t7IeyI9ipzTsGJm2ImqJt6E16mEyWeRC0q70eNrTlhEa7fVfD7fQWZtLHn0PjIVVQcNi2kMvqZNtzXz847F/uV8zAPuh3f9sMM0Zao2lpJFevvnoWaKKfmmGmw4X6UUUehDdqJup/+HikFbc/fj9j929S7xdy1fC3zqxzAx/p4JkpsDObQ87DWXrZl/3Grs1fAKDEUPVGK9fHZvePDHoGXOLm2vCBOn3AuQvEwvzYfP70RZ8UdwT57XYj95mXIXCvGhBk46TXB3/vMxnNcsDpRhJdfAz9pLiJagalqSFlB5MCAEi8UHcxSkqLwuPJFa6vwydtrzzVkOy3+56a3mXLiEKqOmZVP2T1mFgK3N9O32bNUYmMwak4mt+qc3ue25/nY/7oR2fYe2s7H9vd4fmthsDik6sEzzTYg7RTOO1cFlnZqBKd5GcbEmdhvzsvTK5CqiVI9Genm0Mady6Y7L6LiB+f2C7BKDziWrneeJbHHt/C62pCuQ/tzt1Ey5Ud0vHgfVcfMwpeS5gfm9qsHH3Tan5Cuipasxct09luk0JK1aKU1DJ35V4RmoMaTRD+D+ce2IH0fu+Ej6m8/u7fP+OnlGNU7If4N5/NJ8Una65bet096D5y2NJl3/4GXbsljJKEIfDtb8FlLT/brg3ssyPou4BkTZuCsuAWR3A28TuwNfco6hkzHbnoY32kMtkUCK7PAqzwRCrjZa/+GMfggtIqxQTC+2wlglNH4wLx8mvqfZ4WlmpUnLMKOFDNYhagGmr2e2McBqyoRrebAn8zCZhAv3/U+E4/aia7WHOXDixnz7WEYcQ27y0U1FDRTDYPx8uHF7PH9kbxy13thtvzAX42joq4oEHv+hHBdlxX1qzjpjxeyvrWewWU1XP7jGVz59O387qjfMqiyets/MoD/OHxqftmWasv/L/jxj3/Mr371K3Rd58UXX+TUU0/liSeeIJlM7pDfb2lJh3ZJOxqVlQmamlLEdB2hiMCn9J0WJk/bk7uOHkqxLsl4KsOSBqI9ipooR4mW9pt4OU2PYA6bg5QW0s9gDp0Zrhhaqy8istPl+HUHBQqsKx8KxFqK6oKsBr0eo/aqvyBKJxHfdX/aX7iXqmMvw0OlixidLV1bugwgyADYioLrSzRFYPjbrovpuf4twdBVBkc0rj5oJzBVsDwqIhqu9Lf6ve3Fto7/WWLzY++oyedn2V43x3bdP6+daON9yOh+pKxyyOULayH2w1ReIrPuRnIV8wFotyUrUkHp/04JQVT4yG4RAN1Zh6JCpzmSZdp+jMgspc5aBQTBeDqdX+LQuHETJWXl5HIO/W6LiGBYBiUVOcp2SpNO2eFHUsJfmspIygwf5SzKo0ksq7eGsd1spaZzKH6rhlI+EqRP+wdvog7fJ9ynqMgkZkboylrUN7YT03RUAW983MpelZ+/2u6ObO9fpPaqqUY/QSVt7DnU3zsnzG5UT11M+WFnsemWX+cFB/V3nkPl0bPQx54TZrFFtBp9wgVglqNNugqvs5XmZ+4hedAvkEIJJ5ReuqNgIG9vWknnO89T/ZP5NNw1Y4v7gaD2pGtRDRVnxY1ouxyP8KywblJEq0lOmUtKG0yFJ5j3nVq8JWegTrkModfgZ97F3nQTxqDTQKsJayfDCeqg6TjvPRyoCNsplHgJ0rExBp0WZpOsDfOJ7LMQv31NKOgmkrshYjGsNX00SfaaA/QupvXQ+s0pV4JvYa9+En3Yd2l7/W+UTjkKY78FCEXBaWvAE6WkO3J4nuzOtLkovouvCLKZTMFM2xexrcIna699r8EkmIhrikvLU9fmBSqdbz5BxeBfheN64CU/HWvdwmCRfcgMcDvzn9v6SzHHzcR6flrgK16o/CBeg/3WgjAzKPR4n+d2BfrIHyCzzb16BU4q1JkxJy/C9xUa7gxYHm57A6k3HgvPW3ouwoiSfv0vZN9/geQ3folRM4ry756B7+Qo/+5v8XKdKF5/ir7btgnPtpCaSdUxs2h/4d5+wX7VCYtI+d2MPBfIbX3+8VmhSM3SeHt+zX/97WcHAnPeZ9OH/zva69bet096D4rUYKHFWrssr+Sg4uiLUeKl/Rakqn+yADeTxdhnDvZrvQKS2piTQY1gTl4UCLwpBvYbl+C3rcA4YBHSa8uf665bSGTkQqx1Qf+IlsBZcSvG+NMhVhNkxdf9Dbn2CRj0dYSeQBv+PaTvIVwrTwS05zrV6lGUn7IYBKhdLcQVnea0jbk8v8RJLJtDdN+raVndSS7lMGh8Obv+vyF5wfbXpo5DiF6dozHfHhZ+DkEm/fkbl3LQWXti+fnsP91QSHvprdLQLTUTBuMA61vrOfu/5zPn8NNxPPczm+t+ERdOv0r41AH55mrEhVBbW0tDQwOe56GqKp7n0djY2M/bvLKyMvx7ypQp1NbW8uGHHzJp0iTq6urYsGFDmFHftGkT++6776c9/R0Oz/FRFEG8LEKkWKNc1CMeCDxzY8lalBMWIs14IIDy4qMk/1/+xMuo+SnOpqXotWOx1l6av2JYvzgQ97HbcZZeH2ZBjL0uwJy8EAiehe+k0SrG4qsxmu6eTvlh05BSkva3TSf8xGro2wnb8TCiKrVmLFhBLRV4isR2BtzE/1Ogpx8AaePGDiavOLsHwsCNTEHPPIHifESnGMWKFJgKjCwSIGVe6zNkMyCo9lN02pv4OD4eW40yLLO84PHbmpsoKS/vH4wDds6krWMwpYPXUVTURHuml9a+LGey0dHZV27gIyAZyVeD74p04KU84p0lZKtGgKKSXfUaRX0Ccggo6xBYn9VVVVCbMHl/wPpsh6JHUKn0wOvwXAt8n6ZHrgiD8aoTFpGRMeIUtkMTmkbDw1dR/s1u+nhRBfV3XQT0obdPPAxUFaQMJ5Qtz/yJ6iPPxV26oF/deE+5T+0vrkboEZSqzSab487FatwUZOFLioM6RrsTu1vUCAgtyKKTr6LD8VFV8LINSNsKg28/8y72huswh1+CpITIiEWBcJxUkJ4fOGtoMYRsI7fyrH5jg595F2m3h7XgAProE8NgHILaTGvNLMzqc4AIxsSZ+Kk1uB/e2ysKOmkuEig78Ic0P3UL2Y9eo+LwGahF5fjpFmLRIoRmILwcTvNaWp69NaDkd9OXv/QU4G7B0VyXxEu1hIGKOXQsFcf+DlwwB5+Ptf7S4Jk2PYw56g/gdWCtmlHwuYlYZVAfbiQKU3u76sNg3JgwHb+b8SGzDeA74OUQ0XKsZ37b73S9bBqrYWPI8mj/x+39qOfVv7iamqmLw2faeN/FeKkWqk+4HN+Io+gRFOkVzIj6QiPrmcQS5RTvewRKpIjaX1wNqoavmGTkF4MWvkULxa+IECF88ntQyM+98uiLaH36OgDKvvtb6k65GSl9UFSkUBDSx5EuyujpaMUVSL0IYbcEOhaKjvBygchmrjVYMIzGsVYv6D/X9VJIpxVz2GycZbeijzoSKQUy24gSrUCufaLXxaKb5SN9D+k6BdvpqsY0FXobuYcuDK9lyKnX4xQqcZJBXd2qlzcy8cidee7qf+UF2/+8aSnfOHMC+504mpfvWIER17bLFUg3FNZ2rueXt10UZr5v/fkl/Wjoru+GwXgP1rfWU1GURFO0bQrgDuA/E5+LAkt5eTmjR4/mscce4wc/+AGPPfYYo0eP7kdXb2hooLo6oGKsWLGCDRs2MGJEIK50yCGHcN999zFu3DhWr17N0qVLufyglyXKAAAgAElEQVTyyz+P0/9kUMBK2+x34miE00Hr/WfkrUY23Dmd2lNuYtOd06k64mwkZURGXgFCAiq+7WIM2pfcqrODFfU+dg967a+QXjtK8fBAQbebkohQAtELwHl9bh964RyiO+2DWpREaiYlai5U0+zagiqprRRWQ7/tuPGon7J+Oy/49hjoVP6T4LWidT2BjE3BJ8GWFnY8cy+0zDMo6b+w3J+GJmBkAvpF0VYaM+7iyQRSmozofIX1RXuwIbozjjDZk/5BeUdLE7VDh/TbLiWs/WAnSlyLEayjWK6hnfHhZ39LxanQXGRLMCEtMdS885FCko60k0gnUasiGBXDSK98g6KD848TBuQdHdRVVTCoxGR5QxdSyu1amBzA9qHHw7itKYWqCkoPm0HZf50dTPy76x2lphecdKnxJF6qhY1/PActWUvtSUGpTk/f27Nf7UnX0vbsrST2/h51p9yE53p4WhR71wXEShT8VAOeq4dBTG7la7gTDqH16etRE+WBLZSVQk1U4qQzaOUxpGKgihw4CkivYKazo6uNR/71KqdOORSlejKKFkFKE6P8NIRZjLQ6kTkPEQkmd37n+oBiDui7HouiJvGdLoReFmqPWOsWBuJhG65D6KV5YnRK0SDkxvyJnXTqQTORmXaQXiCANGEGRKvQx58ZLCw4DVhLr6P84Fk4B/0cr62etmdvpXjfI1A1HexGUASqrlBx1IX4qRZwshTF0qTVz1ef5PNGj+Do8sc/Zs8j59H20Ezctk1UfP9cDEMgc404a5/C3HUeQlFBaMhcC3bLPf3sTPWqY7E3XAdSIswSfCfTn9q713lI3wtsTZ1UwIwbdgjQXY6RbcL+1yLM/ecVVvtv3UT7P24PM9fW2mV0vHhfIOSmaHhCJ0MMz5WoaoxoVYTKH1+KL7Rgux08S1UV/YKznnpwz5NktEqiVXGEdPGERqS0guaWLr4omgK+UrjP+KoIEcInvwd9/dwV6SLwaXnsynCBVBgxUiTwfJkXfKqqIBo38ADNz2G9ejHGnudgv3kpIlKGNvYMjElzum3N7uz/XlSfiNCSREbOB2GijTwKZ+WDGGN/RXAgGTA/so3ou/8aYSSw31qAMfE8mp/6Yz+WRuyo35Oy/DAYh252R9O6gu9MqtUlXhZhzKHDcCyvYLAtpSReGeEbZwVltdvjCpT20mEwDkGQ/cvbLuKh06/BpLdETlM0BpfV5AXlg8tqqCwuCzLqA1pKX0pssyc67rjjtjjZ3B5Btx7Mnj2bmTNncv3111NcXMz8+QGl9eSTT+Y3v/kN48aN44orrmD58uUoioKu6yxYsCDMmp900knMnDmTb33rWyiKwty5cykq+uJ5ALtCopoqmqFiKjqpPh6dPRkafC+g0FQOQ9BCbtXFvauDw2Yj/cgWasjn4rQvQTP10LLH2Ou8oK7GKMLutj2BnozMLCq+sxBXmih+W54VUGKfS7D0Oiw/X3jC9eUW1NAln8xlegBfJuhdTwAubvRAtjrBUqJ45kS07Auo+s8ZVVrWPxgH6FiPWarhaHX4noMABqffRvctNsZHs8xTGM7r9PQ8uUwXuUyG4tL+5SutDVV0tpRTOjSO9CUxdx10B+SrbZ11js73Sjv516oMRaaGoSo4m9HIUpE2SrLlRLuKMGp2omvZsyieha/21rH3BORNbR0ADCo2eWVdJ01dDlVFhbwCBvBpsUUvYUXtT5c8fj4drz6SRyHuePURqk9YSMOd03v3O2Ehqbf/Rvr1R0m//iiDz30UWy1HQeApJi/99yr2PNik66U/UnnUhWilNXnq02XfOZWmv15L6QHHQkzFjcTxRALb9jGMCAlT4KcLT/TK4qX8ap9D8GQcfdcTsZffgjHudOy3rw8CsORuGHtOxVo9u5c1NeFihHSw1l+GrC+QFXfqEWop5qAZ+K7EnLwguE2qjhR+wXp0oZfgrLkPbdghIfXZmDgDYZbgLL0Gfcwp3Qr3czAnL2Lj09dhrV1G8hsnofqduCv62HTtPZvGl/7cK6D0Jc+U9wiO7nnMzigCak5ZDNJFMxWk1Yz0HfSR38JaPbN37B55FUblEfnj+ZDpSKUYc8iMQLDV6gwWR6ZcHlqd4btIoeO8dVn/2ttuRwDn3ZuDunI3G9TV9vWa33s2jY/d3BuE//I6/EwHXqYDX0oyoqT7Ocnua9uyd/fmwVnfBbJC3/131IlvDYWyvf9Ogbl/B/4v96Dvc1VVQen3ZlB22Nn9nv/m37GNOEVKJ9JqDfpBReu15fvnqShDDkEf81OMSP/3AmMQ1oZr8VMvdJfszEAbcxL2x4+hVU5ACAXZtQHnX4swJkzHfmtBIHqIRuq1R/G6Oqg9+QY8oeI4LqmuDMOrYrQnyvMWI5qfupnq4+bg9mU87TMHXZZRe1wH81+8mQu/cWrhYFsVZLttn1VV2S5XoC1lvl3fpa9iTpFaxK0/vyQvk774F7+jzCzFyg0E419WbDMgP+aYY7b6+Q9/+MPtOtCoUaO4//77+22/5ZZbwr97gvRCiMViXH311dt1rH8nPM8HQ8FQBYof+HuqifI8iljNz68itvvX0aIGuY/P24xOOJvIqCvRq08MO6nws9UXExl1BdLTiXzzLmS2EamXIHwXhB567fYgEH9pR4tWYr2QXyfjvHYRuRHzIJLMs2nQFFFYDV0RAxntryqkjZZ5EhmZ2J0d3/qAsEk7hCHWy+yu/x1LHlFwn0jzEhRNYJu99mICqMm8B0g2xsfgxH127noTAbQ3NwOQ2MzyzLF01n84inhxB4mqNlxLYsqm8PN/dsWIKj4j1TR/7ehiSGkc1+3fkHN6BldxiKWKMWp2Iv3O/+BuWIYydK9wn6hpoGsaja1BQD64JBhCP2jODATknzOEk6N5s/rdjpcfoGTyDzcLvheR+tdT+RZpz9wSqLLTmx2yvKBNGMUK+/9kEAoO8aOm47mAUNFrdqbqR3PB9/DtLMX7HoEwotjSxxWJkG7oeZIOtYREzOmlU/bUsu95Pm57Kw13zWDwqTeGtHe7dNcwkNJ3PTYIvPv0+8jO0J+3Z1tPVrzHn1folfh2K9CF1c2SUmomo+/xayIjF+Hb63Ea7ggpoPaqJwJV4j7OHCJSGfqNSycVbpdWG+Xf/BmND1+OFovivH5JP1u60n2nkln+j1Doq+qUxZsFdl8ueJ4fDofZ7usspxPfbkeYRVgb5uU/Q6z+4/m6hURGXYlve4AMhK8mTIeuTYF/fF/l9fFnosTrQAikaqLv/uvQESCsKzdK8D0H84Arka6L19WBpxRTcdjZ8F9nITUdHw0R11CKqoPs9ydcNNlawP5FR98FBU14uPKrp7K+rUWV7fn+9j7/qJ8C7F6FdSeFUj05cHzQE0gnhZA2uQLvhTlyIX63M4F06rE2zMccdDF63WSk0PHdHEqsBn38mSFTVBt3Lp6nUH3Wg6xuTvHAs+/y8/2H0nZ3MB643aKLLU9dG5YheakWVrbH8HeZz4gyHVXVOfLmC7nq+PM5/ObTwmuZcfIpLLnl3Tx7M6fPPGh7XYG2lPnenIbu2D5Diwfz8BnX4Hi9teYDwfiXG9sMyGtqaj6P8/hSwfN8MooDapzKExYhU0153pxtz95KzfFzkXZrXuYCgs5HoqKYg/Mp691iF0iJdNOgRhGxOpAews8FQjyjT8ZZcUue2rq02xFmsiB90ojAczct46Cz9gz7AsP3ufLIMZz10Lt5NeSG7w/E419RqNl/IPxOvOhkthWMO1LlQ2c8SWUnSu1naYgVDsjjmdfxbB+3fEi/DHpN5n00TWWtuSsAO3e9SXtLEGQXlfTWhktfsPrd3fB9lcEjVoAEx9YxzCyKr9DqwNtZkymJLJkui5aMxZ51pf0U2gEQkIq0U9pVgTlkZxCC7MdvEe8TkPd4kTd1e5HXJgwUAe81ZThgeOm2buMAdiB8Rc+r34UguC759ml5k00hJDLbQWTQCBRVxffKybxXghor7pcd0lRB3N+E80q+oFzzM/dQ/t3f0njfxSHLSboOzY8uxP/uNLQSFZ3As3djZhOXP/wnrjl6GhHTwNh/YcCI6mzBtRSUeCnlh03DzwW+4EpyNFrJCKTQMQ+4EhQPuZl7iFAiBccCoSZC5pSfy0C38nZPpl3f7QisVWfnMaykSCCtLozh/4X9wb35Y0WmPs9vPNxut6Mlq6k8+iKknSk4lmhFve/lV60utwe+0IOgeo9T+o3reF2Fx3N8UCtRVAdtpx/hvHsjGMm8TLfMtQaL7YqGTK3F3fgP9KHfCYP2oDxtNvamlzCq9wLPQag6vjCRrRvxMp0BZf3Hl5LyY0DP4uFXJxDtQU9AWVmZoL0pxVf5HvTiM7oHvguaHi40OfWvou96AvZrs8N2ax64sPB74eWLlvUorftd7bgf3B0IuClB+GJMPA+nrZGsLGFdq8dJl93J+sY2/jr7RFq6g3HoVlu/fw4Vh8+g/rYz0ZK1lP9kEevUBPWdDpc+vI75398NpMDz/TBwvufVx3i//mMuOu5URpbtjm6oeIh+wXbfRbotTZMKZb5v/fklBWnoju0zqLI6EHDzGKCpfwWwzYD8ggsu2OrnQgieffbZHXZCXwaoqgIpjUyxwmUv2fzuwGF5NBlr7TJkLgWivSCdEC+LL7MYNSf3+pnqNQG1DRX3X4sCO5vxZ2K/cl6orO6s/1+0XU7ss20Wzvt3YYw/vSB9MpvyiRTrRM0MMemEteVVhsJtx43/RCrrA/jyQu96HF8bgqcMYlsB+RqrChcVzxiPmnsIw/sYWx2Rt4+W20CEjaSaJX6dQiHqxRDnI1zXY2N8DAo+7c1PApDoE5Cv/2gkqfYkQ3degaalAbDdKNHiFBE3zUtdlUhgdyPF6uZA2bcypm/x3NORdpKZSoqsSvRkHZm1S4lvtk9xPBZ6keuqQlWRwcrWbP8fG8Bnii1RLzOemZftSURcyr/xI+zXzgsngeXfmI2nJKk95WbSJML940YWZ0k+k8hdtojkgWehqJKqI6fhdbaSXf0WxRO+Sd2JlyAVnZSv4XiQI8sv/ngB61vraTj4R2j3XdCvVrPyqAtJvfkYsSPPwauejD7qyNDmyjzgaiDXb0yQKAXHAvQ6zGHzcFY9hVa9L0q8Jjx3fddjsTYUYFgNX4D9zpW948e6p0I7M4wSzMmXYy+7Pk9AzFn5EPrY02m9fz4Vh5xScCzx0h151/lVqsvtQVomKNr1RGS2tf8zlG7h8dxpga4OrLevx9hnNvresxD4+CiYU64ImAp2Cul7uKv+gjHiv9BHfB+MEoyJMwCBMJPYDa+jl++O9dLMPA0ZzygDJ4WaKA9srQaG8QF8XjBiSNmJPuponJUPYIw7DevFaXn9q2+lC74XcrMFPaHX4HeuBU+g7/xDpJZASg9Fi5G2NT7sSnLVy22cdXAZd83+NW0dnYxKKjQVELDTKkdQe+6jrGy1ufCZVt7Z9DYQMEFVCRcdcio3/u9/s/gXvwuVzhvSLViazc/uncG1J1xEXOYz9bYXPZnvh06/Zqsq6wP4amKbo+Zzzz33eZzHlwqaFLz1+Gp2+vHOPPdhG++NLaJiMyENr6sDmp/GGHMxyE6EEkH6ORDFgaiOSGOtW7QZlWc+RvU5vVkNLfBZltkG7NdmY+4/DyAUf5FCCyZ8Kx9B3+8yyDQElihuDlepYtXTGQ78SQLv4wcwhh+KIl1KTI+sn8DLuUHNuDfAVP8qQzirUJyVeMXHhnZlW0LO19nolFGrt+GJ0UgUYtZz2LGT8vaLNz6FlJDLlCC2okNRk3kfXyjUx3ZjY7uHGY1iRiL4vk/zxhqaNgyiatA6iks2hFnvnCymVKRJ+Ot4LTOUnSMOEc9mXUcaRUBZRMPdgrK/pWVxVItoKoFeMYyudUupkhLZR0OjpCjOio/XIgLReGoTBh8PBOSfO7aXeqkrdpiRgd6+0thvATb5wbsi3YIe42Z5LdZL54aBTsmkOTjv3YrfsCTQ45g0h2xkEBmrtz5QdOuE9IXbtgklmqDim8dhL7sOY+xUrCXnIiJl6ONOg0g50urAHDYnVEZXEgeg6FXkVp5ZcCywXzg3OF7tAchsc6+gm1lcmH3ldARe169ejJIYgvn1mxGaif3x42gVYyFagzHudPzdfgbZ+iAYH3U0UiiUH3oGUij9Rcf2mUPjX28C+ErW5fbAt32axCBKtC6MEfPAqQ/HdaFXYK2c1v8ZDjkneFY9Y/iUK7FePKvPvZ2N0/Aa2vDD0AcfhLWktx32MBn0MadgVO8VBuPQqyFj7j8fpSRK9ZFnBTZ/UqCqgriWRkgXKTS63CKsgYBgADsYqupgv3BeIOK287EFhS6FHg/1MqD3vTBHXYE5Yl7vvFivxnltIeZeF+JnW0AooBXR+vd7yO5xFL94eB11JSZlcYML//oev55Uzsr6dsoKCNgJ3USapeh2luZMcD51JSYLDt2VmOezW+VIzvzWTzF1ncuOnkbMiNCe6WT+47fQmGr91Crnju1jEgtqxgcy3wPog6/eMvbnAR9GHTyErBu86Fe93MbCY+bB/TPDbA6JOpSKUxCyPawPFHoN5uDzkE4OYZqFaYp6oMTYU5PTA5ltAKHgp9aEgm/m/vOw3+mmHtZNDm14RLQaMW42o7+eRLQ8Ggz0fVbWI5PmICOD6MzZuJ6PpipEVP0rVWs1gABa5m9INDxj9Dbn1+vtCgRQpTXhyxi2tiuR3PMQ/RmIQBJQeBaxlufIdWrISJLC/PFe1HatwBMm77R2EKuoA+mTbi9m3Qc7UZxspar2g7yfsEgCG/Hs9bR5Kgcl0kgf1rV3UZOIbj1D1E1bT3ZVYZaPIvPBEmTHeijtVXYvKYqTtWxyOQvTNKlNmLy1MU3adikyBrrTzwOaKogb2T6OEcXdjhH925KQbkGKtVCUfkGjL7QtWE5t7Bfo6ONOw25YEv4fm3IlOd3kydMXUBqJkIyV0rX71/P8cLVkbV4NttzluCAYH30S9lsL0fe+COwUmEMxKs8EM4lQJdJp2gJlXQ8o77ucGJQlCQ1jr/Ow37gMaXUWZF/JXDtCLw6806WP0KLY9a+g10wKM/Vhxrx4FMbuJyOzzeC7NP55NlXHzMJXy1AnXx3e/7QXK6iE/1WD5/mYqkKWBIbsxO4zrkdGLSr8DIWJtIISGJltCErQurVgZLYB5/07A+9l38e32vM+s99aiLHnOQizFFAKtnOE2lvGEK0msf8VCC+F/WKviFV80pxARGsgKB/ADoTw3d62+urFGJPmBnPP7gBd6AmEogQLj7Ex6FXHhu8GUuS9P+bwOQizDNm1Cful6UEftf/ldDx/J0P2O4rFx4+nM+uyqdOiJeNww6st/OaAQVT8ZBHNd/VhUZ14OVk3SlcmRUz63HT4zkhPw047JFWBm/Mw4jpRrwgdhcpEcrvo5QMYwI7AwAzys4ACkVKTekty5VG7c9aDy5n+Tzj3qGsYWR7F9zzacg7FMQO322sculcH119GZOQipDAK0xS1JEr15IAGtKJXEE9Eq0HouB/eG66e228twG9bEWQ03rgsb1LJ0tkkplwJyUMLrqxHJl/JNy+4lfWNbQyuSnLrzJ9TV1rylZxofWUhbbTM35HRvfGlztYicsdXqXeSVOntqNIGwNLHY2ZXYHorsLSxAETalqB4XWTWZxF1Y7Z5CoH6+r9wGlZiDtqNVm0ka5dVYURyDBm1FLlZQO8QR0qJ5jdhCp8hSheeK9nQmWHCoCSut/Wl7XSknbKuahKx3WkD7A0rMPoG5Ike67NO6qoqqSsO6jE/bssxrvqL5/rwZYOmChKivp9jREqtKWjjKLcQZEuh9evLuuwoiX0uwXmtt4bc2Gc2ztJ8MVGZbQgUsPv8LxGU+M3EV/0h/G7lYbNpglB9fPMabGm1oo35FdLNoO97KUK62O9cFQTpu09FWm1YTVdhDDm3MK3T09BHn5wvHLd3YC8k9SLM4XOx6+9AL/8OQi1FaCXYK59AK5+Asc9s7DcvQ+ZaMScvwlpyTj/PdH38mVg95U+T5lL7q5tpvPs8qn/8O9KitM/987H/Q4W+djQ8z6ckksFefWHeuO47rVsYzytw3r8B6F78Sa9HH31SWMOvjzoS64U+GfPurLjftqJ7WxX28pswxhUuS+ublZTZBhQcrO7a9J5t9quziE+5CsuOMYAB7Chs3ve6H96LPmkOwsmEfZb5rT+hJA7o70AwfHa+tePqWUTGLcTvCsRdg8Urn5qfX4XQDP7w3Cre2ZTmoJ3LuOLIMUx76F1Ovv99Dt45yZyTFyN8FwsNy0iwZkMTU6+6I5zbLp75cyrNIpY/sZo9jhhFl5JB8YyQXv7X316P7bl4voeuDoRMA/js8MXypfiSQKoQTRgMLtFpswWLj9+Tyw4fTVlVNSLbQeviqWSuPQo921iYVug2I52N2E0PYgw6jcioqwKf2aYHIdeAMfZUpFESeJBDWC/mKzrGXhdg7j8fZ+VDvdR2PVF49VwGcpCFPpO+y/rGNgDWN7bxy3m38f/ZO+8Auaq6/X9un767s70lmwQIJQmEbgAV8BUUFFBEUeClBwggQULgVTq8tEjvXUBRkfqigj8UpYSekEJIQnqyPbNt2p3bzu+PuzubyU4KrwF5zTx/7c69M/fM3HPPOd/zfb7PY7r25/SLlfBlhGK+iyRSeIG92NIiu92O4yFTo67Pv2apOyPQCOWGs4SB/vdx1TKsfhspspVCaEKQ7V5LuLKO9tFH4IWDtIyfD2JkfxTIODmZqDrAbiGTDxMf89SaN7BcD1nfMrXcUnNYqkm5tDOSZmCunldwfMj6bH2fXzNbH/WV1petL9HWvwiE9Ww+YIZhx4jwJu5t2omg73uVH5wwPFamnZGbJ44rSIo6lCl3oB/6a7QptyH0eH6cHcLG7CQpWIskibx1Tr5dH1xJ9fcupuniF6k/41763/odTn93vi1O+2wkNYCz6n+QFdVnNgXiqLucjtCiyJGGwfnBKzoXyMGq/MJ2+JpXI9ws1utnY699F73uP7Fa78Zcfh7miovRxhwCsTHYK1/IB3Ui11ucRbBhSdR7lyN7Jm4ygRCCYLYVRfFLORRFIqJkiUkDRJRs/vXtFZLkjBR1Exa5tTciaXGMlmswRl2CEDmEayN6F+eDbWfp41hzb0bd8Xi/tGCQtQDks+Lqjsf71xlkb3ids7HW/r+i/dz69Lcbt674vRalwrQSti1cyUDf58p8nxRmD7IWLRiz7CW/xWg8G6v7ucLxreNJtJrj858l7A5/d14dZoha7ctY//yNuKkefv61GhrKDM4+qIVR8SCPnLAHz5+5D+cdvANmoAIrXEMwVsn69v58MA7+2va0Gx6lN5Vil8NHce1f7yHrFHqOdw4k+P5d5zPl2uM55s7zWDOwDk0vhU4lbHuUtnu2MRRFxkk6fPTbZex98q6MLteJ6jIftpnsV5Gl64mfDas+9o8Uf5G0Op+yo1aP2DXUW65DUsKAi6zqaAfejuzmAA9PCYGdxBMCZ94snwY5sHzQn9QsLsSDhiLLRY+ZTmEAtq6rF8f10Epm5NsN1MxfEEoVrtzM5rjeQkCHXUG5kkLHHA7dJR1L3QXDnA2hqYBEYGA+ljoKWIsUim1VLi0z0I9jWwT7NIQkETmqCeXjzCb3CAZyAWKBNB8tfZ7O7CL0ZBMQ4S1zNp1SLV8P74UqbbojJ40+KtN1GFVjSK9ZyIbyLeWDAXlnwqeZxgyFsK6wLJHZim9Swj+LTdV5F1P1VhWJgJJByJUYB94GnouQlM3WzDquoD8byP+vBFzCe1+J98GwMrC+71XYix8HNtgM9byigY4nXAa8qC8u9+3z8ews2l5XYH14FVrToX4meuI0RK4/T18XroX9zn+h7X6OL/IpR9BqfwyuLyaIpKHVnQII9H0vQeQGsJc8hehd7P8WkWafyh7fkdyqy0YIuxm1F+GtfXm4nUO2RBuzCDYuiUJQe8JNeE6OvlcfoPw7M8kqIYLZ1gJxvdoTb4ZI9QiBve0FQqgj53VpsASt7jRya31laa3+LGRNJ3D4EwjXw17yVMEmOpJSPHjWonnWgj3/NgC8JY9iZ9p9HRlJBUnBc3KoDV9DGnUYwk7ifPoUIDbBGClN7CVsW8heFnvlCxgH3AqeDXi+ttFg35MqdkZt+AoARuM0PCeFcDqwE6+gVx+DkIclVSWtDi/ViqSWI9dOQRl1DJ3P3YnT207nEzMYN/VBfnXi7oQNFdMGQ1WY32WRsQUhzWFyucr8P6+i6T9q8sH4ENZ19aJVqizLrOSH+38LVVbQFBnb8ki5qTxlHXzP8NMfvYxnz70TgxKjpIRti1JAvo2hConX7l9IIKZjebC6z2JiTYCc6+HYVoHARM8rj1F3yvXgdA6LVygx7La70Jsvxup8crjmTI4h4eTFfYbqaqxlLyC652IceBueEJBpRZg9vtjLxGl+dhzZpyhuYDeh7XsNSSuIrkkENrBYGVpg3vbC3ILv1VRTgaqUdgW3F0hOF3JuHl7ku1sUc+tzw5hCp1nrHFESntMmYjjzCTgLcHNhZDeF5fpZZYJbp1Tan/Cz7t5AE8F572LuewB9TftTsXZ20fM/NavYL5ahzl3GV2sO5r1eh3TIpSnUwkfWp1gpm8MiX0GRivfnVMAPyEPRnehd9kckNwe+BAsBQydo6LSvH2SnSBL1UZ0VPWbRzyph22KTdd4bqXoXo7Zr+1xDUtThfIb6v7RtMuO5X3HWAecwqXEsizvX0r22i4Mnnos04WyQFLptiajwirZLlmR0XUZxTJx0L26ql4HFbxCdOBUpVJcPsITV5ztkzL0Zfc9LBuuHn8LYfQZCiSG5PXmtETl6IHrdiYVzwe4zsOY9DGYvXroDbZczkKJViLaRDCxUo+A1Z/XLI4XaJs8cURIlGyH6//ok5vL3qTzyQmThEJQy+WAcyOHHdVQAACAASURBVC+Qq46eSTBaTTbYuNW/9b8L0maQ6OhrsVb/wg+8a09C0qvRak8it/Zm5MieqNU/QHL6MJdtcA/HXYqVXAVm72BWsXjwLAUqfRFALVrA3vDWvkxu/Vz0r9xM4uUHfHeBDerH9ckz8eRAgaVaIWOkVBdbwraBqkjICMT6uXj1U/IWfUN15AQq0Hc/zXeC6Bgq4ZiBlXgFve4k7ORC9PKvEBh326CoWx32+zeB2Ysx5RbafnVp3kvc6W1H2CbB9GretxvIubBXfZC96gPoksTAuiRzH1mCOWDRfEgtTTUVBUF5U00FQva44KkbCmrFR8WaSNtOgWc4+EG54zkUjqIllPDPoxRhbWt4kO4x2e3ocbzXlmWnuMGCLpPJ9UFyqL6g2waQcLDW3Ya5/AKsdbchDdJwhef6WRFJQ8hlyGqI3KrLN8p2XIE2/of+RJvpQsr14Hz6FPrkmQizB+u9y7Hm3ICwLBJ//R3alFvh4F+T3fcOMsZoBBIZ0yMtNaIfcBuBQ59EP+A2skoj3zloL5pqKgDyNeQBZdOWUSX8e0HJ+laGrjFxi+d22BWoOESlgRHHLHUnBAGCuX8QGJiLQMLss0EPIrStm9K61/qLznhNmCr3TULrPybTsDPBBpNQrJcNU+XLchLv58YhSRI/bZlIFeW0DeRoqQgyXhrN3urOrHE6+SC7aJPXs1WLnJqlLDAJ4bm4nZ8WHI+XRWlfPzyh10d1Vvdmcb3tLxv4RSNtBdH2uaaAmqvtcw1pK1hw3tZQ2xVFwsYh61rYOEWp1p7n8ecFb3LUfT9nXdbl5//zGPFYnHfb2tn/lum8snwp37njPFrTJvLeVxa0S51wEd3P3oyRXMP6F2fRdu/pJF66hcgeh5N49TGQ5Hwm2ln9MnK4wWc02Un/9d7FWPMeRpIUcquvzY/9WuVh5FZdUTgXtN6MtstJG9CebwTP8zPsG0DS6pCMysLfb9z3sFc8hzbpAoyDf+XbbSmBwpKoPS/BE4LUBy/i9LajRCrwJBXZs4uryetBup64iCDbH3PEcQXJXB3q2HvQG6djrbuN3OqrkfWGwQD9BLA78/XkMKwfo+1yEvq+V+N5DtaCu9Enz9iIhn41QlKQI6NBktEnzyw8PnkmQijE9j58pLvA3BsRQhox36elkqBbCdsWYT2LtfAefzwa9CGXgrX++nSfK9F2OWmkLePamwfHtsvRy/fHE9IG62Jfl0ZkO3EzfflgHHyxTCSJnicvYv+KHBNrA3zakyOgycx9fBGv3zYXc8BivxN2Zu3fOnhw5n9utLY9mWv/eNeILHjKTaHKKk3xwjG0KV7nK62XUMI2RqlXbWvIEI4H0KM6hgmGJtGRcsk5OXatKafmpF/S9bhPW6884swCSqGkxRFeFmPUzxEoCMfGXv8SRsNU8Oyi9eaSouIZZeDkELIKegXCqEDeZQaSLOGm+ul87k7cZIJP2k/CKK/l4/U2Ic1kSmMAN2WSs7y8oIuiSJiuRWU0zDPXTcMtqaxvfxACNfMqwtgNT4oMag0UhyNkEk6MWq23+HmSSk7blUDuHazkONzQaLxVCeRY1RYV1sH/yJXzfI/xMRN7kYDRvU/SEFuG0pADIJsqp23peFxX5pauIM3RscDfadZ6eKavBlcIGiIq4DJOaaLXSzI3t5RmrZYGrbrodZOBPsqDuwOQW7cQdtorf6wiFmVdZzcS/lZAQ8zAcgXtyRxNZYGin1fCtoHjCpJKHeENVL6TVnCEoNuWqO2KItHW18/pNzy6WeFKTdZoitexrqcD1/OYftjJ9KYHuPQPt7Cup4PyUIx1PR0s61rL71Yu5OJDboFUN+5AD53P3UluzUKstqVUHnkhmUX/wOltp/sP11B55IV4SH4mevkf0MZ9D5FL+IvW9tnDWczexUi4hfRnJVp0LpCjzQirH3X8j3z6s2VhNM7ASjyXF3ZDjWNlPPT9bwKrB2En8yJh1tqXMb7+KB4KeDbGlJtAeIjseuxlT6Pu5NsXqhX1KNEqUoQIyhnUItZCbmbAD8yLlBJsD3BcAZ6bn9+F3YHnJtGbL0VCIOn1xe9hpBEvmwbZQZv0UxAOxlduRNgpcLIIWcce9HKWa6eg7noa2qQL8namntBZ//wsao46n1zRWnGnYL73UQrGS9i2kIWD0zkb2+pF3fF4hBr2yylkDc+x/bGqCHtHNkYNirklkLQq5NCueJlFfpA+fhr2vHuQ9HB+zFEr6qn5wRXY/V04ve3krBwLBvwEmIRghwMb2PnQZqy0w7wXV2AOWIz/diN/uO5sPNcvMQXBcXsdSfdAgjlr/I36oSx4uVbOQ6dcU1JaL+ELQSkg38ZwJMGBUyeQyzrsXONPeiFNotd0eWuNS0WgngmnPUxMcVGCHuZSf1CSQ7vm68s2VGHVao/Hc01kNVK03hxkJMXA/miWr5h7wK1YWQsv59D9h2vyg1bVT27mF3/t4YojawDI2AKkwozQ1i5SS/j3hmwvQXa7cKLf3mwwDtDjRPGQqVD7N1nTndMmErDnoHvLsUP7IvrfQq4ZPUIhvRjal0VI9nUTCMUwDEEg1EfTqI/J2g18FJxJc+olmsKvUr/jCu57K0Sf8l3K7BxCCGJKHyt7shiqTLkukfM32dlD3YkOO8HrmY/4QezQotT1VKCPKn0XtFA12TULCo7FY1EWLltFzrbRNY36qK+0vqLXLAXkXwA2rvMu1vG2RG03XTs/zsGwcOWz101DQ/Wz5bl+pJzNi6ffxH/96QHue+23nHvoT+gcSOSzKX2ZAZriddz9198w84gz6FjfjX3fqYXt7W1HCcUK/49UgJAQSgC15TsINYykx/wMqNmDcHJoky4Ao9zPpA+O/XJoV1DLi84FnrWW3MpLBinsM0EvQ3gx9LqT8uwqv9TpGuxEBvHJDSN+H6tjJckFr1N5yA8LPa/3vpLE354YrBGfRVatwLU8skqImhNnFdSQVx97GT2v3INaUT+ilGB7wobibnJoVz8YXjEDYXf4HsvF7uHAKux592AccCtCOIhMB7nZPwNA3+967HeGHVG8ztk4gD5xGsJ1sRPLSLx6L7k1CxFHXbDJ/q84A7iuhaLoYJTm9hK2PYbGX6/3E6z3LgcGGRx7XIQcrPI1M4qOYe3odWcghI0sHLSa4/ObWlKg3NdNMC2qjp6JrAfxrCzoAXpfnIVaUU8OlYwtmNue5cBRIaI1IZJdGRb9ZTXmgMWkk1pYm26nobyGzvXZgrXurPPP54a/3MGcNYvyWXDb8mgpb+LZc+/A8VxUWSGmRcmZpWC8hG2PEmV9G8N1PdSoSlmlQUSTydgee9YHCWl+8JtzQYTirMw5WI6SpxRqNcfng3EYovDciKRWI0kuuda7MZpn5M/3F1ZX43k65Pp8X8dAHM9MIksy/W/9jsojL6Th7IeoOe0eZs0TrM/YeMJvR0iTRmQoN7VILamrb19Qsv9AoOMq47Z4brdThi7ZBEV6k+fYyjg8gkjBDI5aDele5K1QWHcsiTULynC9Nsoq40hY1Lcsw3Gi9HW2ICVTLI6cRU/vOELRbhKxJKpazYGBTuycL+i1ojfDDpUhLHtYRViVFPZUxtPnJVmcW1n82oqNqWUIR3cmtXphwbF4mW951dXjC7vVRnRkCZat3/7ouV9WbIna7rheUXEfx/VQFIlM1zLuPPkArvnWGO4/5etcdeBxzDjsVAKaQcYy8zTGu//6G375o5l0JXu48Y8PEg7FRpQlDWWMN/xfCcbo+8dvQfE3FiTXRCTXgBZDDtch4WG9eylYXQgPjOaZg7Xjp2G1PVRkLrgSlHKMlmuQtDi51htBcsEzi5Q6XYZeXYv+lZuQa6fkfx914sUkXn2M5Psvkvjb79D2uYHAoU+gT7kNW6uh7ODTqTnzYTLBRqxBirPrCrLBRmqnPkzzjGepOnomPa/cg5tMUHPirEGv9+0TQ+JuMDi/r7k+fx+cvtd9b+WN5nOnb5VftmCuB2sAKVSPftBdGAfcghxpQps4Dblil/w1vM7ZuOk+sms+pe2Ri8itWehvhLgS6u4zR9Dd+7oT3HnyAVz7rbHcefIBZLqWbfeq+CVsWyiKRF93ArHbxRuVU8zAXvwInucgFAWjeWZh/x91KXbn4+TW3giSDJKKpETzx9HKcI0qsnoFau0OKDGf3ZZ4cRZuMkHFT2axKO2PNxlbkEvavHTlO3zw26Xsc/x4Dv3ZHmRC/hxtW4xY6150x7NMO/g/OXzCQfz57Acp86IEFZXOdDffG1RZ/95d57Oqr6SyXsLng+13+/pzwpDK+t/vX8h+Z0xkpSwzvtJg97oAqiThCIEme8TVCO//eg37HPff6GoXkj56WMDNTWJ3PYWXWYQkeZirrkTYHVhuz+A55aBGsdoeQq/8PtbCB8Hs9evHAuWY2SDx787EdR2W9Vrc9MduKiM69x27E7adYUK5TE1ZBDtrFbR9c4vUkrr6dgLhombfRAT2QKCyObszR8j0OBHq9d7Nq6VLCg7N6KGlOOnBwDhUtsWmtC2N4toytt1GZX0zlQ2rULUc6xN7YKcHqMm9RHqnHVgcOYu63os5b48s772eZVdpLWZOIajb2K7HuAoDsRF1tl6uokoq50NzCeONlqKq6ymjj1h4An2dbyDSPYCv+hqP+YuEzvU9NNVWoykyNWGd5YmS9dm/En65jY3jeqjIyHo9wU1Q21VFLiruoyoy5Pp5+KdH09O2GoCettU88tOjOe+xt8AoY3RlPbcdfykXPHU9c1Yv4pHXn+Hpc25FQsbVDGpP/CWdg24aakU9tSfcRO9fH/KvO5hB7vrD1fk6yIqDjsVNJ3AHssh2K4rmb4BKwVokI4aw20AOoTecQW7FzBFzgaRX49lJGFIorjsNq+Nh8CxEbqC4tabZifXeDX5GfuL5SMLDTqdxkwkAssvex9n/OHLR5nzwDUPe64VPu+sKkgRRlBDBmgDVP/pvPEklS2i7zr6mzSCxlmvJrfoFklpZcB/UikOwOp4omPOtjscxWs7Gq94d4drYH16HftAdSLKG9cE1Rb3IpWAtciBG8sPh/lXzgyvoeuoXAFR+YypqLI4SrSFtKdx71j4F/frhoX6tbp3AZgklbBG5fu4963BOvfEJqsedhxIIokQqsT95AK/3E+RAGHP5Bb7rwFD/90yEpOJlfMq4rFYihIQQTl7A2LHB1CM4hiBhmkhSgPLaHak5/noGXIU5qSC9pj/ehDSJzOB8nO4xeeP+Bez9o52ojlaSi5g4tii61t21fkduPmwmb96+kHSPSTgeYPLJ46iNVLKup6Oksl7C54pSQL6NMaSynu4xmfe7JUw+dTeWJHKMLteRZAgq0NXXARnIDlhIwsJe/xJ63QlYrXdvQFefgdX9nF/DNziR+7U0lwEQ2OEevOSb5Mxl6OOnYb1zBdbcG9G+civJDhOpNgKaRE0NzDqmlu6eHn74i7sLqOhN8XLcDexHN7tILWG7gJybh+T14QZ2Z0ve4z2Or+Bfrmyarj4ELxcFA2Rnnf/CFjLkji3RtiRKeV2S1FsJGlsmUFHdQTozjmyvXzuuOkmqO16hs/FofrdkEv818SPOGPMWamuOdE4jGnXYMeoSUyG3EclDkiQmqGP5uz2HxblVTAiMZAOkAv1URv1sVG7tx9C0LwDl0TAS0LY+wVBleV1UZ0VPKSD/V2Hrym2GO2lA0XjoklNGnB9QNNyclQ9ahtDTthrPtRGuIK5VEquJ8uy5d+B6HpqsESDoX8eCTLCBmjMfzm8E5JQw5d+ZSeWR07HaP6XnlXvywXj/608QnvJDFncKyoLV2BmVMXUGsmv6G6x2FrvnJbTaHyMhF58Lxt1Jbvl5BfOGVnsSuBZyMFbcWjM3kPcXV6bcQcqKEAoZNEx9wLeHk1WycnSDYHzLcF1Big3F9ba/YFxRZFQh+WXZAiyvCX3snUiSVVB2IGu1/vydfLPg/cI5AXQPSQ6h7TUDPDevfg/DXuTaxGnYC+5G3/dqTKWc8Lcvwj7gDOKxMImnL8v3r7ZHLgKgYcbzrG3v3GS/Lq0ES9hWcF1//NRVldYHzgPAGDWB2mMvQAwsx7c/68izdYYQ3PlJYHB8kmSfwalWYYy9DddVebvHIKJZBOw+Tr9+eNx++JJTaKqrItfvO52ENInJ5SpzH1mS/+x0j4lqKLz94CcccuFkkoq5ibWuwoLnV5IedE1J95jMfWw5V59wPpf/6Q5mHnoGVaEKIiKEp8i4g3Xkmi6TclM4noMqq36NeUkosYTPiFKkta0xqLIOkFg1wNxHPqY5axHTZDTZZXX3Sq5/6X7KghH2/n4NXueVg8qSVxaKuwkbvfG8wf9HKuWi+Ltzwu5AMvzdbZHtRMLjg98u5X+ueIc5v/0UXdMYyJj5AQyGqej9ZraArja0SC2pq2+/ULP/QEghXLVli+cmnCia5GyVkrKcMRGuhKYsH1SX3nyGvH1ZANeWidWsQHgeu09REJ7KQF81wtsg2935Nqs7P0GUn8mygVq+OWo+nhC0poNIssSxO3nk7OLCUrVynGqpnA/NxTjCHXHcUWzk8nqQFFIrhm0ANVUlFgnT3j1sOdQQM+hO26SskZ9TwucPBxd7YD2/+dkxPPPzH1NTHt1suY3rChrKy3j2umnMvu+/ePa6afngXVF04g2jC86PN4xGHhwHXVcgWRqGGyYkomhuIB/0K4pEkAyyZ+ezxJblkXKDuJJO4qVbRigEL17dyXeufJyvznyEQ2c+wDn3/A3HqIJwI1JkHHrtididv0FIetG5QAh/g2pDpWJZbwQ9hr3qTxgtVxdSQxtnYC95yn/PoNCd6wqSpkqfE6HPK6PfCX+mYLwE0DUF0h6v3foRL13xDq/d+hF9GZNVHQlyrffmywy0muMRbHpel9UKcquuAM0CaQACFQXniWwncnQsypQ7kMvGkcl4OEqUQLweXVOpPOICak+8GWPUBGCwj61L0NWfzffr0ZP247x7X+QXT79HJGiUaOslbDMMjZ/JvvXEvnoizRf+nprjrkIEGnxP8kFNjA0xpIckRw/EaLkSIQR21xNYKy9BAmZ3GfSagohsjVjLnnbDo2QyaQ5sDPIfY8Ic2BTkkz98SmLVcJlQOB7ASjuke0yEKwgomh/Ib7DWnXXuD7n8oeeo+GoF8ZZo/r3pHpNRFQ3cdsTPSTxtMufWlfzj1gWItIuuKQQVFZFzaevo4rzHr+V7d53HmoESrb2Ez44vbF905cqVXHLJJfT19VFeXs6NN95IS0tLwTl33303f/rTn5BlGU3TmD59OgcddBAAl1xyCbNnz6aiwn+ADj/8cM4+++wvqvlbDVmT+PpFe6EGVaykxcfPL+fDxz/h4Ol74KmCqnA5V33jp+DqlDUa5JZ0gFo9TN0Rnm9zs+Z69MZpWIlXMJpnFIq9tVyJcP2gfyjbAT7FMdXj5jcExh3SzHttWcYEi1PRu3qTaFUK2mA32HCR6pTU1bc/iByK+TYiuDdCbH6BJgT0uhHiamqrxNnUbCciWI4eSSDHqxDSSA2DDT977SchIvEctt1K49gotU02yeQu2OnCWvX7V7WxaPmdNNRN5/9l9ubs8X8kXlXPC/Ni7D2mlz2rMnya2nS7JqjjeM3+cJNZ8kw4Szi8A8ll8wh8dfj1eCwywvoMYFWvyYTa8BZ/jxK2HRRFIpjtIP7yL3B626mrqOf+U69m6iOvb7bcxnUFGmr+eH6cM8o47fbn87T1eMNoTrv9eTDKUPEtfYay3+kNaPB+O1oLBM5qTpxFNtjo11pTRADthFmcd8fLBe16+d2P6UoeRVAxiIY0VDeDVn4cIGE0z8zbZQ0Jf4oNMtG+80Y5nt2JrNWi1u0NShnGuFuQkPHSXVjz7kP0LgaKe7iX8NmhKDKyDX+/f0FBds2yHTRV4CXezJcZyEYLnqQXv5dSAPDF4CQ54KtL73IB1uz/yl9LCtZida5CBCrRQpX516PZDto36Fu1P7keCQu1rIZ0ez9/emMVJ137G9598maOmTqD1IvXM9DbTmajflpCCf8UjDLOvu9lDM0jEpuM3b2GgbmvEp38bTws1KadMFquLhSabJ5BrvUejMZpeFY7dusdefq6kHztJQBZFF/L2rbLq9e9R7rHpHFSJRO+PYa+dSkCMZ2Jx+5ItDZEer1J46QqkP2xflRFBb+/4iw6B5Ik+lPc9OSfmLN0DR+vbOORs06m56Ek4Afzmqqy8Ik1Bc/2m/cv5JCf7sFfb5+bp7ffduLPueCP15Vo7SX8r/CFzcRXXHEFP/7xjznqqKN44YUXuPzyy3n88ccLzpk0aRKnnnoqwWCQxYsXc8IJJ/Dmm28SCPjCN2eeeSYnnHDCF9XkzwxFkbENjQWeQ6bPJaSpTD5tArrt4kgC1/KI6mGyUYW5vTZ7WQrh6IFIEuRa7/ZrappngjPgB+hqJV7yzQ3qBf1aMyEUcAYHssaZWPMezIu2vP7AMDVRj+pk+ly8UHEqeqI/RW1FrGDBuslFagn/9lDMD5BEFmcrvMeTXhBHqMTk5IhjQgiWdrfx9qqlfNyxlvX9Xby0ez/PL5U5ahIM1LgkB3ppjhTPkg90G2T6Vcbt1Uf7mi6+8f0WHEcl2VcGYji6frtngP/X3ctRDXFmJ9LsUnsYrvcyocoenltVz5UHCaq0XmB00esA1MgVVEplzM8tYzdjLNJGzgMpo59o2UQ617xAtZsDxfdOj5dFWbh8NT4tV6I+6r++PJEpBeRfMIJk8kEu+CrmPH85V/34WjTls4tfuK4gVLMD5z32Fp5r+5lxowwJiEod2LMvwxms543ucw1JpQ7HFUXb0fXERdSc+TBZJeRnzgMR6qc+iCfBJ20rUfRKuvoKn6GmmgpkWSKkqNiygieHCBgqiCy5jgc3qjt+EL3+zPx7Ja0OSYtjtd6L3nA6crgWc/lPN9jMvRrJiPu9dlDoLmkF2R7p5dsSqpAwB6z8gn0IVp9NRrWp1uryZQZGyzXIgbFF76Ux6hd4noUcPdCf6wet0IYU04f87YesTBvPeRQwRvQ9JVqJoto4C24ml+1kTLCWi4++lJueX8SlF99K8onzivbTwpKDEkr47JCAeIWK/d5l5Ab7bOXBV9D1xwcxmnahvHksHtpGff9hvMwihP1DcisuHP4srY60rTC5Psjc9iyeVHwtm+02889e63xfB+MbM/bE1lTebcuSWZMlpEnse9KuCNsCC4QD2aTFMZfeVdD+dV29qBF/3gjHA0w6qQVJYsSzne4xC575dI/JwifWMPMHZ3DcoxfgeA7GVvxeBWUusu/U5JYs1bZLfCGcikQiwaJFizjyyCMBOPLII1m0aBE9PT0F5x100EEEg/6EMH78eIQQ9PX1fRFN3CbQgjrvtWZ9SzF8pce5vTZquZF/wGTdYG6vTcYWrEmG0BrPRzh96C3XY4z6OTgDCLcPO/EKqH7939BEbi6/AKv1biQ1DGodgbE3I9wg+q5T0SZdgNArGHN0E/tM24VDLt6LQJnBV5qDpDydB4vQc55+7f1SfXgJeSjZ1xFyGZ7ctMVze5woIIjIwwGy7To8O/9dTvj17Zz81F3c//ZfWNPXzQF1fklFpxnHSXlo9VmO+8sfuPCtV/ikt3vEZ3etCKNoHmU1SWSlnfG7x8lkx2NnhrPjvZbNL5etZWwoQFl4LyQ8dqsJsNren9G13TTGBE4OItqm1d+HsKPSzICXZq3TOeKYqzgEqnZBuDZu6zDVuLIsRs6y6Uv6378soBDSZJaX6si/cMieXeCDDX6QsVNjJdHIZ8tQKIpMIBJACwXQymuQQ9W4agzXFYT1LPb7lxXU89rvX0ZYz262HbKMnzl/4DTW3XQU7fefAZkBxrZMQlU1bjnvRwVj8y3n/QhFSJimjea1o9vLEXY3Ahlh9+TnAt8OqCcfSg+xpzzXQa87EXv9y3hW60YK65ej7X4e+iG/RplyB0lRN8LDvYT/BTwwkzbheKHtYesbXURCtSijhhXVneTHIKlF7yUAQsJonIbT/7bPgEPGOOAWtCn3wuipeX97p7cdz8oSihgoOAV9r/IbJ+MsuKmgr0Y+uZ7jvroTqaxZvJ9up57xJWxbhPUs9nuF46T14VWU7/cdYvsdgfAEkmJgtd6d7/teZpH/fOg1GGNu8G0CtTr0lmuY0x1gUVeOibUBassjPHzpqQXj5cOXnkpVXTlfvWAylS3+WqN1fgL0wWB8g/X44vUmWccm61qksdA1Nf9ZQ2iqqcCIKew5fQz1P47ixCwk5BHPdjgewEwWlkSle0yqQhV527QtQVFkRNotKHMRaXfQH72E7Q1fyF1vb2+ntrYWZTBboSgKNTU1tLe3b/I9zz//PKNGjaKubrjW5NFHH+U73/kO55xzDsuXL//c2/2ZIUkYisx+TUEOGhViv6YghiIX5B6ELJGxBRUBhXFxDcntw17/Ekjg5dYCLkgaWu2PEU5qpDVE80yE00tu2RmYK2aA3Y015wYkRaNnIMkhF8/ixNsfptNN8WGHybwOk9EVBnKonN9efRbPXX8uV556FI/+8Q0u/OFhpfrwEgAQbgrFfB8vuM9W5cp6nQhRJYsyuIh77dOFHP/Erdz82vPoisp5Bx3Gr44/i18e9WNOGO9nqL81akeyn9qMrpO5cPcdWNy7ntNee5Gb575F0vLrYF1HYv26IDWjssiyx/iJGTIph0x/GUNZPCEEv1y+jrTrccHYJt5I7cTk0DoiK57m9j87BDSPXxyawjQlAtqWLfua5RqCGMw3lxX/bWobkSSF7KL38q9Vx31RunWd/oaCJEk0xIyS0vq/AJ6sFbUay6H74kBbCUWRkQIGs1tNXl2ZYXariRQw8osjWTgFvs4wXIO9uXZIwhuROe98/GfY6TSf9ClUlsf476nf5w/XnsN/T/0+QUPnkT+/QSzuoCsSkhz2s6Vuuuh8Mj09SAAAIABJREFUIGkNBMbdRmDszVgdTyIrOnbPa2jxQ7A7C1lowu7AEx69ZoT+bKAUjG8ryLDinTb2O2Hn/MI9HA8w8fAxdP09gSSXozdOw9jhPrT41/E8b6Tt2SBtV1JUzOXT0Sr+A33MjQgRwOrrp+3hi/LWZuD3LXv9arKpPvocpaDvKZGyon110pg6KioqivbTUulCCdsCmxonlXgDkpREIBAUsT1rnonwHKx1t6E3/hS9+WKEXA7I9Jou767L8vfVWUbXVfOrc0/htVkzePa68xhQYryecPlYVpl86m5UtsQIxwOIjdbjBzQHCNn9HH3pnUw567855ud34epw//STCgL8+356IhWBMIEKnYa6GqqD1XiqYMqZuxY82wdOncCKd9oKvmc4HmDATvHQKdcQUSJb/K1UIfHmoAg0DFPh1S2UDCqKjCErGCgYslIK4P9N8KUcgd977z1uv/12Hnnkkfxr06dPp7q6GlmWef755zn99NN59dVX80H+1qCycssPyD8DTZXYrcZgTru/KxfSJPasD6KpEtHqKI7jYHq+CuROVTpBqR9h96A1nI9MDiGH89lxvfr7SFolubY7RlIUG84F/MWVXD4WbfLF2MufYXXwe4BPuZl646M8duU0PulXmNOeZUpDkHSXQ7kXoLwqyFUnHk15KICiKgTKdST5nxd1qa6ObvmkzxH/yut/Htf+vPvrhrB7XkbCJli+J+HA5mmLOVchmQwxLrweZMH1rzzLiws+YKeaeq7/7vFMqG8oYMBGvV5cJYCUSZNZbhPZI8iJE1UOqDuMJ5Yu4vmVS3izYy1X7v91xlk74jkyVU1pwpEE5eUa7/89R0NzGlX1J51fr+nknd4kZ4+po1WMJ+UFqE9/zC9mm5ieSurIGOPHZMgsUghHHSoiKqnc5ihgMjvRzDxrGSklTaVaSKV3457vR/7p+9QdewEAY/QaJKAj0cOBe/viSaMrQ7y1opfKqgiy9PmLJP2rn7eNsa3769Z+P+GFqT3pl3Q+Pmw1VvGTWSwyQ+xRIRPdys8xHY9/rEoXZFTea8vytZYwAVVGmHaeOjwEKViLohlUR6MIL0zdf/6Sjl8Nt6PuP3+JhCiakdSETV/OA0Ls0KzRmejDsh3emreYi3+wN4qUwcu1Ync+jrB7CIy9qeh8YIz6OeaaG9CbLkCvOwk3k0Y4kxCUDWddh9qr1aGoxhfed75sfRU+e3/d3HcQnmDSkWOZ/9IK9jx2RwJRDSOi8fFfVrPy7Q4mH12F03o3euM0cq13Y4y+HJTyorRdiXPQR1+B1flrtJqz6PjVzwB8O7OnrxrWHzj2MnpeuYfoD65jbjrGnj+ZRe+v/Y0fz8wW7atpT2P+QKDg3KF+qpdXEZS37cL+y3jfh/BlblsxfJb++q/8bpscJ6NRrLa70RvPRxJm8ZKN5ksHmTxXoDddQNKGXWsMFnXl6DXdQTuzHB/et4SvXjCZBf0yGduf2zO2YG6fw8RjdySoy6gKBevxCeUuU28sFIQ74ZoH+PWFZ/DIWSejhGTcjMeaP7az4+m1jI43YKYsPMfDweX+ub/l6z/Yl/GhetZnerl3zm8441s/pG9dOl9DfuDU3XBjDvFwGaq65fAq3ZMtSoVXJJnqap/dtfG9FJ6gry3Fa/ctyF/3q2dNpKohsk3W8SX86/CFBOT19fV0dnbiui6KouC6Ll1dXdTX1484d+7cucyYMYN77rmHsWPH5l+vra3N/3300Udz/fXX09HRQWNj41a3I5FI4XmfT0agujqK4wjmtGcxFJmJtTq67PuOuw509yYJxgIs6MoxuT6IIQsUrwc724ZqjMUjhKQHkKnGaByLcLMg6Xla2xAkrQ5JiSCHdkXYPXi5tUi6QbblJK655c/589Z19SILD1DI2IJcyuJvN31IZUuM3b87lnfvW7zBIDIBKaz8U3Ur1dVRurtH1hN/UfhXXn/ja2+ryfDz7K8bIzrwV4RSTdKMI8zNZ3k7bT87nB5Yy5nP3MG6vvWcuPdBfGe3ySAE2Y387bVsF5ZeidfRg+fIpHM1hIzlBOVxnLrjbhxU08jtH8/hnNf+yMXGKTQYUaLxLIHAhyT7LDrXGdTU+6ou/9OR4MFV7RxcVca34lF+trSO3Jp/8Hi/xegywdR9siTUKTSHX2W1F6JWSdGgp/g4FRjxPTZECw0sYAUfDizla+HJGx11CFfuRufKP5BKJMDwF0YVsShLVrXS1+erzFcaCqbjMX95gsayrake+99jW/b3L2N//azfTw83Unv2o1iWhSkUFuXCjK8MkBnIktzKcS0QDeaD8SFkbIHteCR706iKTnSfa/K09aEa7L6MjpP026oYhZZnaUIERQa1or4gKPcz+P70K0mCqrCFsEwaK8r55uRqEDaekJGUGEbzJXh2F8JNFZ0PkFSMcbcgMHBzLjhZ1IodcCQJY+wshLUuH9QbLdeRdaN4jo1n2ci6BoMCi3bW+lxqF7+MfRU+W3/dmu+ghGQm/2BHJA8GOjPMeWZZXunZtCMYzdeAGsQYexNIBpIkyA1anQ5B0upAuCAHfJacZeYz4omX76L+9Lt9K7SuVfS8cg9uMkEOlV5TMCfQwK6nPkS56uEpOlrlNXnqsBSsRdv3GmyjnJxrMocGJpz2MFHFRUgKaRFiILHl8p7Pgn/1mmBz+KLa9q/or//q373YOKnvezUCybdvlDyEU3wsE7KBUnMabtfDSHoz89sD5NwsE2sDLOg02bM+SFiFI679CkpYJ7OisM9mbEFsTAQrY+K4gmU9PtVdlyXSqYGignBCg48eWpp/zc+uC3pak/nsdTgeYOoZP+JTawUXvXQjc1b7gnPH7PUNDvzpbuiShpD8+m/Jkum1to4pZ8gK4XigICgPxwO4wqO7O1n0Xhqywuv3FYpHvn7fAg6evgc5759zefm/tkn174YvhOdQWVnJLrvswksvvQTASy+9xC677EI8Hi84b/78+UyfPp077riD3XbbreBYZ+fwbtsbb7yBLMsFQfqXAQIwFJldawwWdJos7MqhSoAsEY4FAZmOlMuirhwhZQC752+osb3ATYM3ALl15NbdhWeuAC/r145tbFnTPINc2wNotSdhtFwFko6VeIaE6TFn6Zp8W5pqKvAG6ZohTSIQ1gjHA+z6zdG8++Tiz0yRKeHfGG4vbupDvOC+CLa8IO9xIsjC4tzf3UJPOsn1R/yI7w4G4yMgPIJ2D5ZegTzQB9EYA2Y9Ei5VZb4n7o5l5dyy39c4dvTOlCXr+MBbyMLUbAKBbv723CrCkTBJx+GOFa3cvqKV/SqiHBquZ/rf4yxfMB9hDnDSpAyXH5CgVuslsbILWXJQd9wRgNGhLetQBCSd0XIdS63VmJ414rjasAPg4S4Ztj+riZexpn24Bn5IaX1Fb4m2/kXDsjwybhApUk0wVsluVQbCzH22AFP4rKYNEdKG3QAcV5AUdShT7thkDbbrClJukAEv6ludbaCuPkQTVivqiZ8wi3V2mIObcxxU34/urqRW+YDaWMavq1x7PZLdRW711WSXnIS1dhZCjReleXqeg9V6D5Ldipdqp+v3V4CzDm/leZhLTsBadxta43SsUfeQkkbxl5VZZreaCN1g4XqrKD2/hM8O1/XIeS62IgiU6ZgD/jgSjgdQDY2E04AkMlhtD4C1Dqvr+U3M7/eD2w/CQlIVjFETqD9jFjXHXYgS1pGCMdKfvIGbTBA/YRaL0n4mrdcUzB0IkQtWkbR0kt5GfdWrw8laTGkMsFd9ECNSQZoYSSdYEm8tYZthw3EycMgTMHoqroiAJ5C8HHg2QikrOpbhuajlB6HUnQ+SxB61ASbWBijTJSbWBvi4K4flgRfUSdle0fFakkEPGcgyTKgJIEuwsCuH5UlF68XDUb2Ain7A1N0QghFU8tkPLkK1NWYecQZ7jt6VpngdZaEIiq5iCpec537mDU1HEhw4dcIIKrwjbeZ59IoLzG3F0q2ELzm+MMr6lVdeySWXXMI999xDLBbjxhtvBOCMM87g/PPPZ+LEiVx11VWYpsnll1+ef99NN93E+PHjmTlzJolEAkmSiEQi3HvvvVtFCflCIQQ7V+vMHcyST6o1cAW8uSbDlOYQ6cEBpNd0sR0Lo+pwhNWOpNcjrHas9S+hVx9TaHE27raitDa54Uxya2/wsx7NM2hSK/Lqk001Fdw/8xTaTY2QJti3MYhkORzy0z2QZIlATC94oEsP8/YN1XwL8PD03bZ4rhCQcMLMWTGbjJ3j2iOOoyFatknrs4DTh4xLTo0hD/QjVVVh2UEyVg3lgSX0qI1YThBdUfhe+b60YdAfW86EuqUsbnd572/trPpaC/P6U1ie4IiqapKdDVw+V0fTFapaduDaXd5Bw8rT5DOdHZjNYUJxE5JQo/cDdUXbtyF2Ukax0mvjE2slkwPjC793Yw2yEiT10euUTfKtGKsrylm8ah25nIVh6NRFdSRgRU+Wg1rKt3i9ErYtXNfDTZlbPnETsLMW+zYEea9tuORo34YgdjaXP8dxBf3ZDdkWWw5kXFeQDTbmM+dCVnGC5ezqrEF0PYaoPhpZr0cPtGAuuwBhd/jU5kFLLBgsTxL2JlTWz0avPgar6/fodVOJH3YydudNBe+1Vl2G13wn5qBuV8YWvNuaYWJtgI5UNk/Pn9IY+Kd+wxL8fqiEFQ6evkdeOdl1bBrCKcxllw/e25vRG6chhLTJ+V2gIcwUtT88F7trFnZbB45WhzHqKsoOOYWKb59PTomQW+dvAG7cX4v3VVG6vyV87hjqe1FV0PXcL6k/+yFkRZDreAa98SxkIYqPZQ3ng9uHXjGF3Lq7CDVOx/RiOAKWrrfIuR6yLPH22gyGIufV14fG670agrzfapJzPfasD/Jxl//35Poga3rh/pmn5GnrTTUV3Hv+ieB6VP4gwPhQPQN2CjfqIGytaNBbHRlNd6qHx46/EUkBTdXI5Yaz0ptSTNd0mZSbwvEcVFklokSwLa/oWLFFlXWZoll1ZErr+P/j+MIi2nHjxvH000+PeP3BBx/M//3MM89s8v2PPfbY59GsbQo7axEOB8jYgom1OpYnmNdhkrEFkgSLu63hAcRRCegOyAEkZJ+iVnkYVvdzhZ7kwi5Ka/Nyq/M+jbm1N2PscDtPXDUNQxboikw0EkYgIwkBWZsPfr+U1vkJwvEA+5+0Cx89vzxPpys9zNs3lMzrSMZoXDkOYvOdoMfS8dBYsOYDrvn2cdRFNk9xClrrAbAIoGTTiMgYAHpTzQTjPTRUzmdV5z6ATKqjDFl1uOKbvcSCEr95wUYIMMNBDi+PsoMa57EPykhaEoeOV5kbO4xjy972g/ECSCS6JOrrP8ETgnjEJVxRRbp3/WbbWi5HqJEqWGiuYHdjR+QNBcE0hWjVnvSueoOY4yCpKrWDwm5t3esZ09SApsjURHSWrc9s9jolfDnhuh6KmWNKY2ADGvdnzLJv8rPFsKWUB2WiH7frMfSaH2B1PY1WeRhyYGx+nJeUaMGYL4d2BUkuTllXo1htD6FVHgbCQ42UYXd2FFxf2B1ENZf57cPPSsYW6BvUHGZs4X/vEv5puK5HfpnuARYQGPQXH7y3khJFeP1YReZ3IalIkoQIxLFXXFaolL/mCoyWW/CyKYK6y4GNUZ8Ntw37awklbAtkhM8OUnQBIodWeRh290voNccUH8uUEJIwQbh4yTdxnWnM6/Cp6hNqdDwkHFeQsQUZ282rr+uyRECTyNgevab/5M1p96nu767LMnfw7yXdZTx73bkkExncjEfXmwmajo5x3KMX0BSv49dn3kTYCyEQfO2cSSz806r8OrlxUiVhL8T836xmWc+H+Wy2MljumVdM34DmfuDUCRhlKqv613L6o5exrqeDpngdD51yDaNiTfmgvGCs2AKGsupvbnSdzWbVS/g/gRI/bRvCdT2kQdqjLkuokpSvSZSAnOvlBxBVLQdZQ3gmAg/hmaDWoVcfg514BSE8ZL2BXNv9GM0zCqk9LVdidz2Vv66wO0AI6iJhyoSBnlVwUxZ/vfZdXvjZ67x6y1zGf72ZypYY6R6Tdx7/hAnfbgG2kiJTwr8tJKcTxf4EJbrfFoNxIQQvLW/H81wOG1e3xWAcIGSvx5MU7KSftfEivke36xn0JMcQVDtprJwPwiHTHWKvb7xIWayVlLUn5Z05gpEQ1+8xjt3kOu6aXY6hwA1f72egai8q1Cx760uKXnd9TwBZ8nB1iQD9TD7yeEbvsT/SFkQgd1KaSYssq+y2EcfUxok4dh8smgdAzWBAvrajK39OfVRnRU8pA/V/Fa7rYaZMzGQWM2V+bsGNhONvwHY97We3W+9GCDs/zgs3mf8bQKs5nlzrvUXmgqsRzgBa7Y9BrQNJQYlW5K2D8tfT6sg6Sn6xCn5G1dqgJnVDen4JnwMk1Q+2B++tcJMg6cWpu0LCM1eBSCFphaV9wu7ATa9nzU1H03r3ycg9K7Ez5ufaX0so4X+DIXaQhItndyMp5ajR3Tatsi4F/LUwMpJWh+mqZGxfmymoySzqymG6w6VFQ+rrH7ZnGch5yBRuMA5tOA79bXkCkhIf3rGYT59dw85fb0bTVOb91wu8evaj1Ck1fPj7pfzPFe/wwW+XssfR4/Kq7ZOOGcvbD32yyXLPTSmmC8fLB+MA63o6OP3Ry0i5w5axn+039ZDCCodeOJkjrtqPr02fiBO1kbde37qELylKAfk2hpXJsW9DEEcInA1qEnOOx76NIXKux7vrsrzbZpLz4qDVIzwPtFpkNYLV/ZwvfCHrCLsbL/kmVsfD6I3TCAzS14VQ8tlx8Aczx5KZ8+tPGOjI8Mb9C8CFQMyvaU33mLz75GJ2/ebo/P+x2hBHXrU/B0/f458WdCvh/y6U7OsAyOEJWzz35SXzUQKNpDPtTKyt2arPD1oJbD0OA4N13KFhX+h0rpq+zBhigTXsVP//+OaP7qBh9EKSuQkMDJTT152gvDrO2+tkbn47xNgKl+u+1suAWskKp47/CH+EtAnv3GRKx7JURAQM1SSxZhmj99ifvb77E8pqNy0EWS9XE5aCzCtigSaaxyDJOsmP/gZAJBQkFDBY3jqc3aqP6nSmLNLWPyeuUsK/N4RQkZTyAlaU8ETeCsvueqpwwaqUF50LUGKgVoEURNLKsTruw1x2sm8dVHfGsJ/v6GtBKc/PRyFNYt/GEKv7rOH/G4LY2ZH6CSVsG1hUYLRcjZ14BaN5BnbiFf4/e28eJ0dV7v+/a+nqbXqWnn1JMllJQgJJCAFZlYBBAQVFQBS9CgJeEAFxuT8vYACvAorIooLCVwVFLiBb4BI2gbAvIQmTkH2ZfV967+qqOr8/eqaTzvSEBDOZDjnv1wuS1Hqq69Spes7zPJ9H1UvSobo73FOz/Y8o2KS6l6BYvbiqL846juKqwhpIq+dbfW103ncVXmRUjiQ/sW2BUHRSXY+h6MUoWjGKMHP2exUbXFWYkbUo466noSedchFLCRyRNsC39ZssqPVljWVzq71s6zeHTTAO/dvnSgssH1ZmoFoOC6+Yy7wzp7LupSaEBa/f9iFLrn2LF3+7Ypjz6qjzZ1J9boC+WGjXudsj5XbbZIxxgHkTZrL49EuxHIukFsNl7LkZpmqwOdrISb//FjOvP4XTbvsujaHmj3UsSf6QZ0nY+z9DYY8Bn4FNuuzZ8rY4qzpM5la5OWrcdoMklIhT6HLj0lRQdBAJXKWL0t70bTekQ9ddVTixNZnQHsVVhWfyr9Mz7IN55lr1Yt76WxsHfXocK5/YTLQ3QTKSYuZnJ7Ds7g+A9MBg+NO32x/0IFTSiozSDj+g0eOv4BhTUfVC2MVHXW8swu2vvcxPzvoaJc5mnF2WERtECLypbhL+CagDfQhNw/G4wdm+70C0mkSqCCIWiZCBr6aQWNRBU5IMdPdQPHEKN73uY3KJw0+OHMCFxdPR+RSrkbR3fESHnkJ3r5vqiihKv0L/imdo37KOqQs+zaGf+wrtGxrY9PYr2KlsA0RVFKaqdaywN9Bl9VGubxeBUVwG/rJD6G98hULzShRDo6a8lA3btnvTqwNpdfWtfXEOrtx3Zesk+xfRhJdCfykonoxuiOIK4qq5HM+UW0HYCFzp8ljCBD2Q811g1KXL8Cl6CQItHRJq96a3a7oR9+RbsW2NcMKLwOToOi8oCpGUw+beJBNLDGZXpMOdzZgMdx5NknEL4a7HXfc9hAB33aWAMoJyvoar8lzMjr/jrrs0633vqriKjgfvyGxv9bWhjjAxKZHkA5FkAQWV38Dsfgqj/LSR028UBZRiVP9MXm/z0ZfYblCrCpw40YeqKiiO4JjxPkxbELcEm3qTTC/zsLY7kdk+nUOezEw22gNJSFi89dcPM6Hen7pgBqse3Zzl1X7r/rXMO3Mqy+4eVDIXUFNVQYHw7Tp3e6Tcbg0WzTqGsxacTEVhKcW+ADc88QeWNrw6LHx9t39PO5LT6/7PS2/Hje8j9pbkK3I6ZRSwbYdYOIEVS+LT0gPH/BoPuioAG9uxWNu6gQvu/WE6T9xsIdV8SzrnWytG0YsQqfZBL8nOIYqLMbueHJxZvB3P5FtIJquZeGQNK5/YTM/WEP6gh0Q4lTHAIT0wmFFLhqhLMiipbajWVoR3Ph8lTvXLF59kXEVa9M3ldO1y2yFcdhSXk8B0FaOGB2AE8bdE0sd7z5/Cto1HEA0lEXaKWCRKIhpnWVcJ5T7Bj4/sx1AsVpoT2WJVcbJ/+Yje8SF6er0oKuCFaQV99LU28e6TD9C8ZjmVk2cy5/NnYXj9w/abqNWio/FBctOwdXrdLMxkF3ywAoC6ijLaevqIxNKTGdWFQ0rrMmxdMjKWLRBKGaorkDHGjarzMbddQ3LbdTjJpnQKieIi2fhLzKabMt5z2K7Iner4K4rqIbn1WlRMzJY7MarOHyyL2Y7jCAaiHiw7LRQUD8cxown8GkwrNfBrYEYTxMNxaYyPMqZpYyYthBAk1p6NsKIIxx7+jh/3QxwrAnZoUBfAxpj0W7zT/oZ74q10P/1AphQapFX7HUX6ViT5i2k6KJ5JuMrPwBEGQii5+30qjKomUBXBkFZaOprHS3IwlSg2ECMajpOMJNARBFwKs8oMVDPJrDKDEyf6OLrOi08THFbt5phaL6v+uoZnr3+L9x7awLwzp3LSD+Zx1KUzMF0pWlb1ZLV1mPNKAbftw1HUnIrouCCpxYgokZzrNUPlikXf5NrH7uC0W7/LV/9wFd8+7svMmzDzY4evW46V5XWHtFFuOXJibn9GjuKjyJBhDuAyVJrCLXSGern/9Sc4a8HJ/H+nXYShK5gtj2CUn4Gw46h6MUP5M05sTSZEUdGKUYzydFm08KvYg2OI4qrCM/52/vXwhsys3xFfn866l5qYckw6NNcf9HDsRbMx/DqfuWLOR6s4Sg4I9PjLCFRs10G73G7Z5nX839qV3HDWr9BEEtUJszsB2d5UupMm9QLUUD9KRWVOgzzWW4SVcFNcvd3Q72pJlznsM8r58ZEhPKqFJVSeiCygWu9l3i6942kGQgZWSkX3O9R5eoFyHNti8/I36G1r4uDjPsehn/8KK5Y8SCq5vVSZoejUq9VsNJs50jsLn7pdqVgdNx11pYeB956iaN486irLAdjU2MKh06dS7NHxulQ2dcvSZ5Jdk0oINMXeQVV9u2E+VGlDKz0Hz5RbEWY7Qi3AqLscRfVkFLlFqhdhhwd1RNLHGlLwTuelD3/F/7tq9JKPj2na2C5tMId8AFX3k9xRyNUOp9PWShehaAHAj7AGEHol/bECQFC88ELM1vVYfW3oJdVUnPcr4vjYHcV/iWSsUFQVIRzMzd/HM+XWEfs9ug/HMT5SXDPnOJYjVUzTVGadUk9/c4SerSGWP7yBuf8xmeZwC7OCB+X0ag85r46+6GDiWhy37iFlDldEx0WWYNvJs47l5u//KKsueb8ZHubN/sE/bmTx6Zdy/r3/nTGk3XvwW+qqTl2wKssorwtWoas6u/VxJslLpId8HxGxI9zyzJ8ZH6xiacOrnH/vf/M/T9wFwsFVuohk082oqguz+ymEomfyB53YGsyWO0FY6dzx8KtZxxWpdlQ9XdLspB/My+TFzD5lIkW1/kyeOH6VuG19rFqJkk8gQqDFX0G4Z+EIz4ibWY7Nr19+ivqSCoLFk/HSg23v3ojvG1RYT9ou1EQcJ5BbBK6/tQJFcQiUbxdHe+Hd9Ivmy0cUUu5JC8K9Gp9Jt1PEFwre+kgBujQK3b0ehBfKvX3Z52xrZtULT+D2FnDwiV8YJvY2TRuHg8Oa5JbsI7rc+KoPp6f1JdTuKFWlxbh0jbVbm9PrFYWagJuNvTKnU7Jr7JRDf6uJ4qrKKG+7Kr6aMcZV30xcRYcjnBTJxl+Savo5yqDHPLn16kzJy1TnA2kDT6Sfy7SCdzHGhBuIJrxjfJWSnYkmvBj1N6AoLhxHYFR9A7PlThKbLk9HOAwJuzoJFFcQgQscM/PeVnwlVH/n94z78ZPUfu+vxL21so64JP9J9kNqYDByR8Go+mbOfo+iE41796q4prvAxcIr5nLa4iP59JWHcMkTP+PMOy+nJ9rPEV+fnu3VvmAWJeMLqD43wLce+klWfrZtOyQdmyTpuuMhK9vYfqZhGVc9fiMDaohe0UeMCKpCTm92sa8Q2MGQ3gMKtAL+9K3rqQtWZY7xp29dT4Em0+T2Z6SHfBTZsSahgZ/LTjqP7kg/dcEqKgtKufWUnxLpdggUFyNS7QjFhavocFJ9y9AL5273hjgJhF4M8e5MLtkQiqsKx9EpKPUiVHAXGsz9ylQsRRC3BsNXpP0t2Qk1tRbV7sAKfI5ddZDHGt5jU08nP//CRQjFhdfpGXHbnfGlOjFdxYhQumyI8A0PDxcCBlrKCVT0oWgWCNjQ7bBhUwd1bj+fqnewUhBzDJ6JzWO60cwUrXHalwIBAAAgAElEQVS3xaC7e71UVcYoKokOWxfu7mDt688z87iTmXz4sWx886XMuoDqp0Yt44PkJg71TMW9w1CpTZyD07wM852X0D9/KjXlpXy4tSmzvipg8F5LGEcIVFlGSpIDTVMRtmDtv0LM//K1CGcgyzAHMsa5e9ItuMf9kGTTzZjt9+CecA2KXoiTbM54yd3115Hq/AcwWDaLQqyoQw4HuWSMsWxBOFFFoT+K1fUoevmZeCb/ejDaoT8t8lf+ZYRegtn7IkbwBCxbR9MUvPEWOu67KuMdr/rmr8FdM9aXJJF8JMJJgeZJh6nbfQitaHi/r/oGDi6svTTBNFSK7IW7VmaVCCsvTFcuaOnrwH5DY96ZUzH8OmbUouGZLcw8fTzXP/M73mtcDcAF/+9qHv3e7Rg75WfvHDo+b8JMvn3cl/nSHZdlSpzd8+0bWDTrGJY2bHem1QWr6I+Fsgzp1B5MOqRMh/GFdfzz0tuH1TaX7L9ID/kooWkqRG3+9ZsVvPH/1hBpTTClYAKzg9N49rv3cu+5/0PDfY289fd2FFdp+mMMB7PrUXRPOQhQ3RNQ9DJUTz0kE6CquCf8LCvvZsgLoqhK1syd9IJLdoUWewmhGDj6lBG3iZlJ7nj1WQ6tGUdN2cx0jWN7DwxyswvTXYYyqAhMwXCxkfhAADPmo6S6C4QgYQlu+pdFMNVNTV05tpX2+j0dm09cGJzmf2uPKjMNhNw4FujFFi5tuHHc3biJ5rUrqZk+h5LaCVnrZmj1JIXJhzt5ybWKelzeSno/fBolJaitKKOxvZtkMu3Jryk0SFgOHRGpWC3JjQuV5Y9sYNqnx/HuIyah3jqMCdcNekWHlNUH61VjZ5TYjeoLEVYPZv/bqJ563OP+C8/k3yBwY/c9nRH9av/LtXTce5lU385TLFsQivrRgycCINDBqED1TMJdewnCVYHV9b84fU+BomErPrzE6Bw0xiEt5tb+lx/IeyzZL1BUVzp3vH4x2P2kWu/EsUIorjJUzySMmosQwkYVey/da6RSZL8946c8duGdPLb6eSadWMXyhzfwwm/eZ/nDG5i+cDxrHmvk1lN+ymHj07o5zb3tJK3UMBXzodDxIS5ZeC4/+MeNWeHp59/731x7+iVZ3ux7vn0Dh46bzj8vvX2PBd2GSJkObtuHXxTitn3SGP8EIA3yUcKFyrK7GvAUGhz6hUlsfLWFaFeCF25ZwdPXvg1RJTNIOMKNu34xjhAY5V/GbLmT5IZvkdh0BSLVhUDDbF1McstFmB3345n8a9wHPYBefzvhZNVem02UHCAICz2xDOGZhyNGLl7553deoScW4ZvzP0VSq8RLH7a9e0ambscw7CimEUwrrLtcOIYxbLv+5kpQHAor0uHq975t09qXoiDZTXlNGUIIWqwgy+IzOca3lnKle88uVSjEexQUP0wqyf3huvX9N4gN9DLtqBNRte0uxTK1mAo1yPuJ9Vhie5i+oqi4JxxOqP991A3NjK8qRwjB+m3psPUhpXVZj1ySC01TEZagZVUPK5/YzMQja0il/PS0lCH0abjrr8+qV+2kujLvhaHwTpdvIo6AZOtdOMltqJobV+VvwLiYjgfvINnYINW38xzLFsRS1aiYpPpfA1SE2Uli0xUk130NJ7I8XZtZaOipblSVjDGeOYa8x5L9BMcVAFWkdWRUXzpkfdti4mu/SmLTDyDVSarjb+nJqb120tylyGK9Sdr+Hua/jr2YouIC5p8zjYVXzGX+OdPQdJVEKEXDfY38eOF3gLQR3THQPUx8befQ8bKCkpzh6QoK/7z0dl7/7wf456W3My5Qi8vySkNakoUMaBstbEG0N8G8M6dmyii8df/azOCQCKfwBz3M/WIZWC2gl4MdRuglO4Wql2B1PpIJY3TCr5LYtBG9/nYGoh6kkItkT1GTy1GcMJZ7HiP1n65IiD+/8wonTJ1BbUkt3WoBPqc1pyhbLnxmWqAtqRei9fdCcRDHyd5XCOhrqqKosg/FZfJuk8MTaxxOr+2GLYJgVRAh4KHw0fgUk8963v1Y19vdbuCvTHL0hEbWdQ8XsHNsm/VvvsScRV9i/CGHs/X9NzLrDtbq+VdqOatjm5muT8ws1yfOhbVLiLzzNHXnXoDbcPHehxuZPW0yVQEDBVjXFeXoCUUfq82STy66UAh3xvAHPfRsDWVKU/qDHj59ySF4CoPpcmZaMe76n2G234+r8tzs94LqJtVyB+7a7+IIF2ZCo+PP12QZbFJ9O//xuGKYXU9glJ+G2bcMvXDesFQ1q+sRdPccNG89ekm1vMeS/RLF6kOk2jGbb00LVlacnzNkPZooYK/lWY5QisyMWhlv+fxzpvHy71ZlrR8qe3aQr5q6YBW3fvW/cIQzTHxteOi4llNsTVM13LYvva/NHoWnSw4cpId8lFBUBX/Qg+HXM2UUdhwU1jy7jSO+Ph1fsYqCA06YVOut4MTToequchSjBhQ/ds8/so4tUu0oipwVl3w89PjLCDWAo48fcZs7X3+elOPw1UPnk1Argd0vdwZpg1wACdWLOtCHUlzMzsZ/tKeYVNxDsLadUNzhllcsJpQozPG0AFBUVsK7ySlstqo5reBt3Hy8ULaufh/CFtSVt424Tairjc6t66mbdRiegsLM8nKlhDKlmHeiH5LawROl+YN4ymbQ3bwUT7fDpNpqlq/dhOM4GJpKbZGbVe17VspEcoDgQMPTW4eLCX1nFrbloIgkyS0/IbnxYsyW29LKw6ionnoUvQzFqMLqWYITfhVhhVEUQcx2U3Her9BLqgF2Ut+W5CuKYqEHDgbhYLffBmgoRhWKXpq+z10PYff8A8VdiMAZdo+rvvlreY8l+wUCgWpUZ5xLduc9mB1/Az2wPVVDLyO5Fz3GliKGlSI74uvTWfPsNiDtLdfd2VGCQ9/r/qCH0qJi7r/wJv5nyV0Ymiun+NqOoeMFWkCKrUk+NnJqdZRQNIUjvzEDK2lnlVEYMsp7toZY91ITn/pmNSLVBnolRtV3SG5bnFbYDRyDu+YiFNXOKeSWq5yNRPKRODG0xJs4vmMRIyiVb+zu4NEP3uHLh8ynxOelV6vEJSJg736uos/sJOUqxgmFUBwHJzDcU9zXVIWq2RSUd7L4eYtQAq4+UWHrM634iwKkPIU81nkkE1ydzDPWfuxgEFN3Y3U5GBVJygIpusOunNttXv46pXUTmbzgeFa/+CSQVk0/RJ/Ci6l3WZnYwHzvjMz2ev1cIu/+Hef95UyZXs2HWxrZ2trOpLoaJpV4eKMxhGk5GLqc95TsgAqJkMnKJzZnxISspI2dsvEEDCI9Fp7BMd+JrSG59eq0Xkjd5SS3/CRTs1dYIYTVhaoHsG1B3FtLxYX3oAoLR9EzhlqBFkd1Ujiqizg+qcidRwiho2rFAKiBY1AVQWLzT4a97zGKELZG3FtL5Xf/jGqbIGxUlwfk3Lwkz9E1BewQjtWT9T1r9z2dTs2Y9Euw+lFdFWiasdfGKNveXqpMcSDUEWPlE5vp2ZoWmvUHPVjJ7KoxQ8uOuXAWjfEWrv7nb+kM91JeGPxI8TUptib5d5BfiqOEcASaS8Xl0TjmO7PY/GbrMI/I7C/WY4ciCCpQFEg23ZgO5Sn5PEbV10ls/iHJbddlSqBBtpCbRLKnaInXUISJcB864ja3vPw0fsPN6TMPwRY6KbUUPz3Yzu6/VHypbkxPOUpvOudbBLIV1h1Hob+lguKabh7/MMmbjYLzj9Co9ifpamyhckIND/UfQVR4OKtgGXuk5LYTjqYR3uiACp8/eOuI25mxKI0N71E6fjLF1XWZ5eVqMRP0KlYk1hNxtk9KGHWHoOoeejf/H9P8lWiqyvK1GwGYXOoj5QjWdUvBJUk2liI49qJZJEImy+7+gDf/8iG6oWH4Xax+dhvb3ovjrr8ua8x31y/Gim4AyNQad9d+N10ea3Be3bYFEdtLyAkQsdPvB2+8hc67z6f55i/Seff5eOMtaDnEDSVjQzThBVcpjtWHu+Yikq13457w0+x7P+7HCJFCRUHTFESkm7a7L6TpptNp+d235D2V5D1+Txyz/S+gFeUc24SjIJwE1kDfXu/PQ6XKUprAU2SQCKV1cPxBD8deNIuCMu+wSKWiah96gZYxxu/59g0E3cW7ZVhLsTXJx0W6WUcJRwhWL93KpCNr8AVV5nxxMkLAwsvnIoRAaAJdCdN51/ep/NrVqMHCTF6NUXoSiY2Xp0uhpdox2/+IUXc5qns8luMiHPdKITfJx0KPLkXoddhKBbnytN5q3MiyLev47lGfwdBV4loNKCpuq2O3s7p0O4phRwm7StD6mxC6juPxgLN9JjrcXoptGmzTt/HH122OnKCycKJFf1cfyWicZMV03k1O5XP+9/ZYyC0XoU6NkiRMLm9EZTLOCHORLR+uoGbqLCbNP5blTz6QWT7XmEKT1clb8dUs9B8OgKK7cdXNoafpJWo2f4/xVeW83bCOL51wLBODHhRgZVuE2VUyXE2ynSGvzWd/eBiWaacrZERMEiGTSUdWU1yVwmy/DaP2krTSuh3GbL8Po/ZiROQNnNiatIdJOLvMucylyt1531VUXHgPEeSEbj5g2YJoogifkQIUsHsRevn2PHI7jNn+x3Rpu4k349cELfKeSvYzFFVglJ+BEEnMjoeHjW3u2ktwrFI6H/o5drhnVPrzjt5yHEAFoYHupKNZPQEDRYVYf5KG/9vCnLMmc8d5V2e83MmENKwlo4s0yEcJSxHMOmVipuTCUO7Kyic2kwiZfOaKOWiOnVZJ9RaiaD60qstwBWYhzK6skDUntobklp9gTHuAgYgUcpN8PJTUVrTUOuzCcxA5PuBtx+FXLz1FVaCIEyany6El9Fp0EUVxQrt9Hr+ZVkw3XUVofSugqARnJ+969+Y6Wunkrre7mBBUuPRTDo6w6dqWzh9/zncatXoPn/as2CvdPelyEW808U2Fzx3UzFPrcufPO7bNlhVvMv3oE6mcPJ2OTWsBKFB9HKSN50NzK1OMcUwYnOF31y8gufVNQhv+xSGHzOPxFe+yobGZg+rHUV1o8H5rmK/Prcp5LsmBi207eINuurrCeFUdK+nw1n1r8RS6OPF71STCr5IMv5q1jyIuwlV1AcnNV6Y9TKpBNBkcMedSdVJSlXs/IGk6aKIcwwjhqr4YVVjEt/xk2HaKqiIcqbQu2f9QFYHZ9Sju2otxcoxtwrmY9vt+TrKxIb39KPVn23bIuAUccKPx7kPrOejT43jpzu21yo+9aDY2Cn5RKEXYJPsMaZCPEkOzcQuvmEu0N4EZtbJyVxQHhOGh6lu3ohUUYQ68gVFyNImN30/PHsq8ccleRo+lw1tt4+CcRu7jq99jbWcbV590GgA2blJqGcVsxbbt4TuMQEGiDUfRiDsGnv4emDqdHU+YCPl4ZWuSR0IrCfoV/nsh6IMihe2btuH4S+n2T+KKwOMoe+nFnHIZRFaF8E7xc8TE9by8uYpIangZNoDOLeuom3Eo9fOOpnPrBsTgtR+sTaTV6eal6HucVXgiXtWNXlqPXlhJV9czzIssYqnh4pX3VnFQ/TgmBb283RQiZTu4NJkdJMmNYwlWPLaJeWdOpWKyQKQ25xz/HasH1VW+PYQ95dqlAJKjuvDNPJ7AYaei+QqxYyHC7y2Rqtx5iJ2CF+9u4qTvj0OI1AjvfwdVR95TyX6Ijavya6DoOfs2tkPx8d9E8xXimHGE7obdq7D6sdA0FV0oKA60rOohEUpl9DzMqIXh14nbcpJLsm/ZZ1+JW7Zs4eyzz2bRokWcffbZbN26ddg2tm2zePFiTjzxRE466SQeeuih3VqXr9i2g4Pgzb98yLK7P8gSkogPJCDSTfdjN+IkU+jFx4NIIVLtpDofwD3uhzvl2Vwv88YlHx8nhh57EeFdgJNjYieSTHDbsqUcWjOOudU1ACT1WlAUPHbHHp2qwGwj6a6AznYUIXCCQSAdnrm8yeHHj1s80L+CaeUqv/y8wKelALAti9YtzbTVHMMXC9+lUtl9VfePwjQMnKSgr8eHUmBz1kHrd7n95vdew+0PUDdzbmaZpmgcqc8iKVK8HFuOEAJFUTAmLCDS3wDdzcyuGsfbq9cTjcWZHPRi2oI1nTKPXLILBkXelt39AVYiQarjrznG/+tAcYOiYtReAmoR0WTuCaUhTM1PycIL6FlyC613XUTPklsoWXgBpubf5X6SfYumqShAIpTCsW1AH37/x/0QFAOw5T2V7Ie4UBQNx7Fz9m2huTJ9uvuxGyHSM2q6CJqmIqI2//rNCvpbolnlJ1/4zfssf3gDzr+hWSORfFz2mUF+7bXXcu6557J06VLOPfdcrrnmmmHbPPnkkzQ2NvLss8/y4IMPcvvtt9Pc3PyR6/KZkcouqFaYjvuuQguUohgOqojhJJvTnpDYGsz2ezBqL8Ez+XY8U24lmqqReeOSj40eexZFxHB8R5PLPX7Hy8/RE4tw/oJjsIWDAOJ6PW4xAHZ4t8+jOiY+s4ukpxK9eStCd/FGj4ebnktx9r0mP3k8RWM4yTmTqvjpQguPun0W+r31UUiZFE+aytHGql2cZc9JGunnr2dLOk9sUlUrcyt7Rty+v6OF3pZtjJt9OLrbk1lerBZwqDaFrak23o6vBsA9YT4oKp19SznKqsOyHV5b0cC0Mh+6qvDq1v69ei2STxY7viPcfjci1bvD+H8rRt3lCCeBohfhmG2YLXdiC+0j3weGHaXj/h9l5Rt33P8jDDu6Ly5LspvoQmH5Ixs44uvTifQ6KKqSrsk8dP9rL8HsehRFETjCJ++pZD9EkAo3oKpazr6NbWb36ft+gJfRmcjWhZJJJR0qP5wl6nbRLCxFfmtL9j37xCDv6elhzZo1nHrqqQCceuqprFmzht7e3qztnn76ab7yla+gqirBYJATTzyRZ5555iPX5TO27aAMhq4vvGIu886cysonNqPrDlZfG2Vf+E9ItSOs/izPiBNbg9lyJ6huLNu7V2szSg4whIUefQLHmIGtlA1bva6rjbtefZFTZhxKbWG6BndKLcNWAwRE6+6pqwsBQuA3O1AQxIQftXkbSwfK+ekSi7e2OhxRr3HBuOn8tOZYTp/fjSK2h8E3J4t4Z0U7tsvHl6b1wF5+Idq6jqW7ULqT9Ib9OIVwyvgG/K7UiPtsXv46mu6ifs6RWcunauOYotXxfnI9KxMbUD2FuGsOprt9KVWOh8mF5Tz16rvg2Ewr8/Lq1n6EnHGXjMDQO+Kkq+ai6GpadTjVS3Lr1SQbf4miuEi1/QEFsAbexV1/PfGIA8luNCs0zJOkaQoFWhxNmJSeeiXu8bMy62S+cR4yGDa78onNJGN+hFAwys/AbLmTxKbLMVvuxCg/AycVRlWTlJ56JTUX3UXleTfjHj9L3lNJXqNrCgoWRvGncFLh3H07EQfAPX4WlefdTMVZP0PDzoxtmqagWaERx7z0NipuVcONhlvV0EZKE3PIKj88VH7ytMVHpsuj+TVsmTMuGQP2SeJRW1sblZWVaJoGgKZpVFRU0NbWRnAwnHVou5qamsy/q6uraW9v/8h1+Y5tOziqwpt/+TAzEFhWOb6Zx+MKVoHoRdj9WZ4RRQsgnARCLyUa0ZBCbpKPixZfhmp3kSr8CkJk54Jbjs21zzxCkdfH1+bOxxmsTR7X61FFCsNuZ2STFXBsAg2v4tu0AsVMUDClCFEObf/3KqWO4E0xjmsWqUyvtBlYP5VweDIVBy8HLZE5RKsZ4M7mwzm06ddMmDEZnSSw98PVEoYbTzTMuqaJfGpmFHeRyZenbOSvH87IuX1soJf2TR9SddBsOjesIjU4gagoCnO1g0iKFG/EP8ASNrPqjyTZ8kc6nGV81jWH34feYNnyVRxSN5V/rOqkoSMq1dYlI2LbDi5fCkWYOHZ0J5XtexCpdN9zlZ/BQHeM2799BL2t2wjWTOD83z6Gr2IKti3QNCVd6mxQiVsvqab8zKvpXfo7ko0N6CXVMt8431DJhM2+cOsKvnLzTKxBL2JGibrrUVyli0D3EV6+hNjqlzP3duC1B+U9leQtfp8CigrCBqsds2fpsL6N+nnc42cRXPSfdD18fWbsqjjvV5gFdYTbNnDP90/POebBDmHoO4goH3PRLLRcxvXg87ajUb784Q185oo5JJ3d18qRSPY2B9QoXlo6uh/E5eWBEdcJR3DcxbN55Q8fpEXegODnvofZ1YwrqJLqWYp73A9JNt1McuvV2/MGhU1JcPfavavz7wsO5POPxrn3Rn8Vjkm8++/gmUhByTSUnTzPd736Iqs7mvmf075ESaEPgJTwkaSaoNqKC9D14bmqQkBqAPyvPIK/bw1R/yysgiJKC9cQ6U9RYCf4sHoOl03zkExo9H54MJGW8RSN30ZBZStCpCfnmpJF3NZ2FGVbn0FLxZhy8HgUJW2Mq+reNcoTPh8F3Z0kIx66QwFKimJMizQxp7KKD7pLc+7T1PAOFfVTmbjgM6x54dGsdce4ZvNWUuOdxBr6i2qZX1RJ77ZHOGjWCUyJlbFk2dv85r/m89iH3Ty/ZYATZlfv1esZ6+dtZ/b2+Jpv1zca7HiNjtkOQiXV9U+M8jNINv4SkWrPvAscM/3327/9GXpbtwHQ27qNe75/Olf87U2C5VVY4R5a7s4ui9X18PWUnnolPUtuoeqbv8YoLsOr7luRwXy8l3vaX0frGoQjOPai2Sy7K/1t0LB0gENO/ibJrVdvv//jfphWqa67lJLPnE1s9cuZe1v9nd/jGoN7ujvk430fIp/blos96a/5cm22nUKxOnFSMVRXgFTP0vTY1nTzDmPb9VhRFyULL8gY47C9pF/VhXdz66AxDsPHPIB4KMmzd63MGNnR3gSv3tXAZ390GN6gO6tNO3+L+4Mejrt4NoESL4V7+Ztjb5Av91Iy+uwTg7y6upqOjg5s20bTNGzbprOzk+rq6mHbtba2csghhwDZXvFdrdtdenoiOM7oeJrLywN0de0611bzqemQGAfi0Xai/f3En7ubqm8vxij/MmbXI4Mzh8UoriBm/1sYJUd95HF39/yjyYF8/p3PvbcG0L3RX/XIYxhmO6ngZcQHsnOyVrU1cuNzSzh+0kEcVltHKJoOGxswZqJoAre5hVgqW+rU7NWIbnAT22JQlnyLCs8atiVOoSN2LBFtG9Onf8BvVkyiaOA0ak0vm1ss7IQXFEFx/UYKJ6zJ5L6ujlVyT8cR+FSTmZv+hl5RSqAiiG3bqKqy15/VmDstihiIR1izLcixs8KYxQan1DWwtvtIErZr2D52NMrm999g6oLjKa2fQfuG1Vnr56szKNT9rExuQK0u4ZC1a2k/Yg0nBw7izs7XeOCJ55k7fjbPftjFNw6toNQ3/Bwfh73Z3/Opvw4x1uPJvmDnaywttHFsE6PqG5jtf816FziOQ6rrr7hrL818mA7R27oNM5GgqytMoZLIWRbLqJ5KxYX3EMVHqGff5hvnY1+FPeuvo90fvX49S+VZ6G48k3+NSKWj58yuRzGqvoFjxXAFKzP7WX1tCFS69/E93R3y+RneV20bi/6aT797aVEsHd1j92MmmnOObUmrmLgmKCjXc45dONYuxzxIly8bMsaHiPYmSCVtIjl+i6FvcRxwuTXilkV3T2QvX/2/z76+l9L4H1v2yZRqaWkpM2bMYMmSJQAsWbKEGTNmZIWrA5x88sk89NBDOI5Db28vzz//PIsWLfrIdfsLtu2QdGyECrE+m3BfN3a4BzAQRg3uuktRPZNAL8LsehKXbyICbaybLdlfsXtxhR/EcR+Co9ZmreqOhrni8fupKCjk4k8dizOYEmEpAZJaHYU0Y1tpA104ENts0PFkIe3/LCa82oO/OMw43zOkghMpPKkUZf679Jb8C4AjJ07m4JnN+Cvb8Jb0UDJxPbWHv0LhhNUMpV68EprEH9qPosKIcm70T0Q7O5h2+Ow9Kq+2p5iGG1vTKIz1E4552NxehlFo4vElOH3ylhH3a1vfQKizlUmHH4cnUJS1TlEUDtImcKJrAf01E0nqOitf+zkdlZ0c5a/n+bdWUG8ksIXgwVV7plYvObAQ6GB1INRi3HXfQ/VMQnGV41ghUm13YZR/GUdoBGsmZO0XrJmAqqUnehzVhV6SPdGtl1RjKwYR25sJ8ZTkF44QLH94Ay/85n2W3f0BiojjCB3FVYbqmYS79hKE4iXVfm86/HcQmYIgyWuEhbD7SfUsxWUUkgo34K69JD22GeUkrWIiMQfbFtiKkXPsEoq+yzEPyISh74g/6BnRwhn6Fk9i4y10y5xxSV6wz2Kcfvazn3H//fezaNEi7r//fhYvXgzAd77zHT744AMAvvjFL1JXV8dnP/tZzjrrLC655BLGjRv3kev2NyxFUFxVhauojsLTf4rd10aq5XacVC9OsgmsXvTCOQi9GCHyL4RGsh8gBO7+3wImduC0rNzxeMrkyifuJ5SIc/VJp6IPhmkJFELGHFQsfPZW7ASEVnlofbCY7hcD2DGNsgUJxp0RYnzgSVRhYs6Yz4auGD97qovP1fcREsUUl/ZRULORkikrCE5bTmD8WjTvAAApR+VvXfN4sHsuM/2dXFz6AltfepFAaQnVU0b5eVYUor4C/KFeVATrm0qIJj3Y5QqzSrdyUMnIaujr33wRIQQHf+Y0VG34JFlQLeR49xGYEw+jvLOd55r/gFW8jSLNw1//9wnGe8I8vrqLTT2yBJokN9FEAbiqUBUbx+zAscI4ya0oIomr7FSE6kU4Guf/9vHMB+pQPiXu9ERRHB8V5/0q82E7lIcZxzdm1yX5aHauxiKEAHsgbdCkenESGzOTMqn+tJ6AXlJN1Td/Le+tJH9R9EyYutn1KLqnHJHqBhRMK0Aktt0QHmnsSuqFnP/bx0Yc8yB3NSOpli7Z31DEAST/O9Yh6zuiaSouRcGxIrhd4DIGMACnDpEAACAASURBVNv/gqt0UTqURy/GQSWWLN4thfWxDlM6kM+fjyHreuQxjNA92EVfJ6UfzJBnOmaaXP74fby5bSPXfPY0ZldVIYSgwO+hIzmOqDGTYGwNobd6iKz1ICwFb41F0fQUrvI4Dg5GXwsVb/wNc+p8VhkTufapLo6qiXDHp9fTos2kI5o7qqMn5eNPHUfSaJZwcsk6FhY2sOb5V9jyzgqOO/vzFJQXZ7YdjZB1AF8sSk17M021U+n0FFLgTXL0wU0oliDe7OOW94/AdIZ7nDRNpbByHLNPOJXeps2sfnEJQgx/LoWVhFduh8Iy1h93FkanixUtCSqNAhKzDkfRLeZXrObw8VOZP2E240qqMznze0I+hgHLkPU9I9c1ug0VnycFTj84CbD70+JuTgJcNUTN0rSXOzmAY6fSXiJ3UZbnW9MUvMRQhYWj6MTxjalnPB/7KuRXyDqkxxhdKINhtOBydebsA5bpR7GSOIqOp7gsL8PVIb+fYRmyvm/weVQ8Wgtm+1+3f9u6gpiimHB4+PtzpLFL05Rdjnnpfbc/P6hpI313PN/59HvtjAxZP7CQsU5jhG07pH2WXswU6I6PgprvoyopUFQcoRNLyHJnkj1Hi72AEboHx7sAS58Ngz2tZaCXK5/4G2s7W/nxCZ/jkKoqnMH5uIioJOqagdreTcsjURAeApMtCqcnUApMhBA4AI5NccNz2N4AbzGe/3myC7+hcP0x7Zi46bN8QHJYm1ZGq/lb12HYqFxU/SZTXS1sfmcFW95ZwZT5symqDO5eebV/k5jXR8plUNHbRmd1gEjczfsba5g/rYmCughnJ9Zx3+qZ5FJ572vdxoa3X2bqguOZ8ZnPs/aVZ3Cs7HJDiu5GTF+EWPEws1rasA7+In5/Fy+vX0/N6vdJHryA5e0Hs3Tt/djWbVQESpk/fjaHjZ/FERPnMK5k7wq/SfYvkqZD0tRwG+X4PHFUVzEIB0e4iMS8WEMfmHoh6INP9k4fprYtiODdYckBM+e+X7P9mwCSSXCLkfqAANJCm/ko5CaRDBFLOOCpxVv3PRAWKDrxZEF6eQ5GGrtsW+xyzEtvs/35QX42S/ZDpEGeJ1i2oD/sAXbMg5GjimQPEDZ65H9xhf+B456NVfBlhGOTsi0eWfUOt726FCEE1518OjPKy3GEwLEgFJ5CSqtHaWkm/kIrxTMdAtNiCMNCCEEmhsZxKPngGYxwJ88GjuIXT/UwrkTn94s6KdFDNGuzMKPZxni/5eGx3tm8ExnPOHc/36h4G72vifdff4eWhg+pmTaRmUfNwdpX5UYUhZ6SUqo62xjf30ZjcTWd/T7eXT+ew6Y2cdBBzZyhaDzaMD3n7m3rG1BUlcmHHcPcz5/Nh688Q6y/J3uj6llo3etJvnk/eqCGyvGHc4xL4Y016yn44BXUiXMIll5KFUm8aiNvNy7nmbV/R4g/U1VYw6za2UwqP4jSgvFETY3OqEkoaePWVMr9LiaXevm0ruPO2ULJJ4G0Ye6GrLssDesDCdkHJJ8EYgmHWMK3g7dXftdKJLmQBrlEsr8jLLTEu+iRB9FSG7F9x2D5PsemrjaeW/8BD654k65omPnjJnLxgqNR+sJsem41idYeqo1exmkd+EUc1aXgqtTQFQ9Kk4GjGwjNwFF1sCy0aD+qkmBVeTWtfX3c+BmF4+rClGgD9Krj6EkaJGMhQt29NLXH2NSt0hbW0cz3WKh0Uyx6eK+3n0Q4gqKqzDz6MCbPn4E1ikJuuYj4A4SLEpT3tlEc6iFqeLE7NDZ1uJi8IMVhM7cxvaaVNxqqWNleR5+dHcbVunYV8VA/048+kcO+8DU6Nn1I69pVRHrSom2KouDMPBUtPoD13E1oC75G9cxT+PS82by3diNd61+nsrCS3vKJJIOTUXxTKR1MA7WAFd3p/6AbhINHtyj06Kiqi7ebBUnL4eZXGqkpNJhXU8hhtQHm1hRQ4JbDuUQikUgkEsn+hvyCk0j2J5wQWnI1OCFUuxsruRHNbMBQEoSSBbz0wQReXbOVzoFfkojF8JgpTlDd1Do2+qa3aV72LFVBNzPKAlTW+NC9GpbLwVPkxecfVGp2BLZlomCiKGkDU3EDBaAoHo4mytFEEQL64yovbITX3nyDRHc7IrY930koKlWGD59Xx+3RUVwGFeOrCVZXUD25Fs1tjI26qaLQESwn7isgEI/hT8RRrARKu0P7kw4VR7rxV6c48ZgmFtqNpAYgFHLR2h+gN1lAJOUmkmqh+7UGSicdQnn9SVRNPZhkLEK4q514uJ9kJIw1aTrhF/9A9K37UFY+TsmEBZwQnEin20VDqJX+TR0YW3XKCoopdAfw6368to5fcRFAJ+CoFDgKHlUnqHuJMkBYjdHrUenwuWl3vCwNJViythsFQbVHMLlAoditUGhAgaExqaKWqko/tcWej/5dJBKJRCKRSCT7nAPKIFfV0VUsH+3jy/Pn7/lH49y5junq/xNaLF1eTKDSFzdY1hwntHUO3U+9RV/3U+hAzQ77BHxuTj/+YDR3ARWHVQ0TEbO1ApJKIa832jy4rJvOll688R4KUn34rBCaAkNaMoqSFk/RNAXbFliWg615iBbVE606GrtkPKXVlUwdX8hxE2wKXalMyLtA7EHEpcK+Cs+0ARQlPfEAtAlBoKuVgLoZzZvA8EFZkUVZfR/Qt9Peb/D8+hZ69Hl4SiopKqugdNxEFDUtbCc+8wW63niU9ufupX/DayjrnqcSmDzlCOIn/ICmjm5au/vZ0ttEMmUxEv8x+3AqNA96XKcqCeN7FbwOaDi0Gg5bPCpbkwqvxmF7YQYb1jUCcM+ZM5gY9I54/L3B3n4Gxno82RccCNcI+Xmde9qmfLwGyN92gWzb3mRP2puv1ybbtefkc9ske5cDSmVdIvkkIISdLg4+iO3YOMIhbe8KFJEumyMGrej0E+4ACkIIVAUEztCKQUN5yGoWaatbCATbzWIFhaElg6u3bz9oZ4vB/3+SUBQVFAUVFaEo6asbrAMsBn9n297VVad/QWXw74oy+PuhZNUTHpoMYIfJElVJ/1thh9972LEH/1Ayf4CqDkY2gKaqaKqK+jGU3CUSiUQikUgko480yCUSiUQikUgkEolEIhkDZM0MiUQikUgkEolEIpFIxgBpkEskEolEIpFIJBKJRDIGSINcIpFIJBKJRCKRSCSSMUAa5BKJRCKRSCQSiUQikYwB0iCXSCQSiUQikUgkEolkDJAGuUQikUgkEolEIpFIJGOAPtYN2Jm+vj5+9KMf0djYiGEYTJgwgeuuu45gMMiKFSu45pprSCaT1NbWcvPNN1NaWjrWTZZIJBKJRCKRSCQSiWSPybs65P39/axbt44jjjgCgBtvvJGBgQFuuOEGFi1axC9+8Qvmz5/P7373O5qamvjFL34xxi2WSCQSiUQikUgkEolkz8k7D3lxcXHGGAeYM2cODzzwAA0NDbjdbubPnw/AOeecw8KFC/fIIO/pieA4ozP/UFLio68vNirHlufP7/PvfO7y8sBeOe5o9tedGev7tzOyPbtmb7YnH/trvv3eo8GBcI2Qn30V9qy/5uu9ytd2gWwbjE1/zdffXbZrz9nXbdub/VWy5+R1DrnjODzwwAOccMIJtLW1UVNTk1kXDAZxHIf+/v4xbOF2dF2T5z9Azz/W1743yLdrkO3ZNfnWnr3NJ/364MC4RvhkXGe+XkO+tgtk28aKfL022a49J5/bJtn75F3I+o4sXryYjo4O7rjjDp577jkeeeQR7r777sz6Qw89lJdffpni4uIxbKVEIpFIJBKJRCKRSCR7Tt6FrA9x4403sm3bNv7whz+gqirV1dW0trZm1vf29qKq6h4Z46MZAlxeHqCrKzwqx5bnz+/z73zufAwB/ijG+v7tjGzPrtmb7cnH/ppvv/docCBcI+RnX4U966/5eq/ytV0g2zZ0nr3F7vbXfP3dZbv2nH3dNhmyPrbkZcj6LbfcQkNDA3feeSeGYQAwa9YsEokE7777LgD/+Mc/OPnkk8eymXmLpikUaHEKlRAFWhxNU8a6SZIDHNknJRKJZM+RY6dEMhz5XEg+aeSdh3zDhg3cdddd1NfXc8455wBQV1fHnXfeyU033cS1116bVfZMko2mKXjjLXTedxVWXxt6STUV5/2KuLcW287b7ATJJxjZJyUSiWTPkWOnRDIc+VxIPonknUE+depU1q1bl3PdvHnzePLJJ/dxi8YeTVPwEkN1Ujiqizi+EQcdLzE677sKLVBK6alXovkKccJd+ArKCNvGPm65RJLuk/3P353pj3YsRP/zd1N82o+Ja77d7tsSySeVrnAvKceipqhirJsiyRM0TaFAiWCn4pSeeiX9L/+FZGMDnfddReV3/4zAJtUXoUDT5LgpyUt2/Ha1wiaa5v63++munouKC+8hgncvtV4i2bfknUEuyWZPZwJVJ4UWKCW46D/pevj6zD6V592M5q2TL23JPkdVBEVHn53VH8vPvBpV1/CGc/dtieRA4pIHr2Vdx2ae/d5fqCwsG+vmSMaYofd+2w5jY/mZV9O79HfpDSJddN73Q+kdlOQto+HF3tVzkWxsQBXWXr4KiWTfkZc55JLtDHm8rb42AKy+NjrvuwovuWsTOqqLkoUXZIyfoX067vvhiPtIJKOJghjWH7sevh7VNveob0skn0QSqSTrOjYD8EFr7ugwyYFFrvd+18PXU3z8NylZeAEdg8b40Do5bkryjT39dv24xxx6LvSSahxF+hgl+y/SIM9TdE2hyJvAcKWoOOMHuMfPyqyz+tpQhZUlamGFe9A0hTg+XGXjMwPWzvtIJPsa4dg5++NIy4f66dAzUOKJUORNoEvRFsknkC3dTZm/b+trGcOWSMaaj3rvGxUTcZVPoPTUK3N+E0gk+YLqpD7Wd2jmve+NUOKNUajHMqJtIx1TKyhJe9/x7fXrkEj2FdIgz0N0TSGgtGO/fhnJF8+DbXdRecb3Mi9gvaQaoerpcKC7z6f55i/Scud/4I2nP+YczYNeUp19TDl7KBkjHMWVsz+i6jmXKzg4tpV5BswXv4b9+mUElHYMQ5XKqpJPFC0DHZm/b+uRBvmBQC6F6N1576d6m2m66Qx6ltxCcNF/Zq2T73dJPuGoud/7u+qnOz4D5gtfI/X65ajxZvqX3Igv3oJw5f621YqqZMqGZL9HGuRjiKYpaFYIkt1oVihjXPiNOKl3rkbE0x9qIt6B1fArSk/8j8F88F+DqmWEsmouuistbvH83XiJERM+Ks77VWbg0kuqqfz2bWiGIr2Nkn1OnOH9seK8XxFXA1R++zZqL7yNcZf9kdoLb6PyP37DwJuPQKJ32DOQeudq/Ep/ZhKq8+7z8cVb8HjUnM+RRLI/0DbQCUBZQQndkb4xbo1kNNE0BY+WxBdvzhrHvPGWrPe+WjID1+xLUN1uqs79Gb6Dj6fyazeiFQTTejCB0qxQXekdlOQbI773d9FPR3oGKr90FZEVz6DaSSpzHDMiCqQxLtnvkVOqY4SmKcQ6N3LP90+nt3UbwZoJnP/bx/BVTEEVFla8I2t7Ee/AXT2ZstN/DAWlKI6VWyhLBTsliHtrqbjwHlRhIXQ3uugj9fplWPEOFG8lgcOvJ6xVYclBTDLK2HZ2f3QUnTg+FFvgMizMTbdn+qVrzn8ROGQhCCtjjA8h4h040Z6dtBGuovqC3/HgLy/jgxcfy3qO5Atasj/QNtCFz/BQUVBKKBEZ6+ZIRglNU0gNNOPze3LmgNddehfWkCEy43zM929GDI6LFV+4jr7XnmbglfuyhKzc1VOpuPAeqbIuyTt2fu9rhptQatcq60PfvrmegZJP/YyUlSQUi5M65kKKyqrwBysx3aXYprMPr0wiGR2kh3ysMEMZYxygt3Ub93z/dEgO4Cg6ircya3PFW4nZtom+F/6EZpsoTgphmWiBUmC7uIUi0gOTbQsitpeQE0DVHFJv5/A2GvF9eMGSA5kd+2PE9mLbAr8Rx3z7mux+ueIXYIYQqDmfATvUm7XM6mvDiYf49Fe+DWQ/RxLJ/kB3tI9Sfwk+t5eBeHismyMZLZIDxDsbIRnNmQfL4JinT/1qxhCB9Lhovn0N/imH4h4/i9JTr0TRDSq+ci2Kx58ZTyWSfGPH974eKP3Ifjr07ZvzGXj3Z7h8XoqD5Tzx+59zw1cWcPN5x+EkQ3gKPGiaNGck+zeyB48Bmqbi2KmMMT5Eb+s2HDtF1PTiWnB9xiBRvJXos64i/MErlJ58Ka13X0jTTafT/diNWXlkaaGs4TOF6gjeRikCIxlLRuyXHi+OcGEcvjjrGTAOX0yiqz1re72kGtVTQEnV+MyyoedIItkf6IsNUOwN4De8hOLSQ/5JxbZNAqWVqJ6CnHmwKduF6/DrUYzinOOiXlROcNF/0rPkFlp/fwFt916GPdAlU3Qknxjitg9jwXX/P3vnHSZlefX/z/20mdnel6UIiKggoCigQRJb3tcSY0GNLaIGBRELKs2oNAtVQRRUCBaMMcVoTEgixvKzgYKxgWJDaVth++7MPPX+/fHszu6wC8YILPA+n+viYtozc8/s3c59zvmeXY4Bt7aU0qXXceUdD9B9wPFUlWymMRZnVXEcEQ4FRnnAAU3QezsAPWIQlRo5nbsnPZ7TuTuKquO4Ekdko/SZQOiUJ1H6TMCOSrJOvoqKP01vt+QDNAliCdlmgd6Vxz0QgQnYl+wsZLTLfhmPgWtT8bfHoPtotEH3Q/fRVPztMVL7/piUvicBJEI3K/+xkHBaeuI9cjp3RzVC+/S7BQT8t1RHa8lMySDVSKHebAQCb+fBiKoahFPTqPzHQvIvvCtZ4+WKeZiEcZRsRDiv3XlRGCltykeWLb8tKHcWcFCgqoKQWYFV3bDLMeA21OJUl9Lw15mMmvNb+p96Ho5iELUla0pi6BGjg1ofEPDDCQzyjkAINloZjJj/l4RRntO5O9c9uhIEYO5A2DG2PTyKHSufRBJi+3PTceu2txvqZhQeSqerF1B4xVxAkCZrkxSoG62If/LeytuoD76bRiuyL791wP9hDEMhTdZAXQl22ZfU/G02XrShTSSIMWgqZB8CUhL99A1KHh/P1oXXUvL4eKKfvgGuQ+7Z4xJChlUrFxP97A10TefGZa/R/5RzGTH/L+gZeR38jQMC/jOqGmvICKeRGorgei5RK0glOthQVQECND1E9LM3qFq5OEmQVcnII9RQTPkjV7Hjn0swBk1tEx3kWLsvExkQcCCToprI+koq/jCFmndXtImQ0/qNp/KVJ4Gmfa+u8ctf30+lyAQgaksQQbRIwIFL4CLtCKTE8mBTuBdX/uYdNM9CDaVg15bx6HWnU1Wymfte/hotu4jUPj+m/JnJ/kKsGXS+bil6di5CSOzqcqrf/ANW+TdUrniAwl/OofJv9xP97I0WRctIFxxXUq92InXowoSoVr0VCQTdAvYJqioINWyj9OnxONWlpPQ9idyfjQPpIdUMjBPm4TZW4tZVUbFiKdmnjUKk56JlFyU2oKFD+pF71s2oYQMhPDzFpeaNpzC3rPfDPXdsRpM2p4+ZSnVWb/IJFuaA/R9PetRE60gPpxLW/KiOungjKUagmH2w0FrA9bbHX0HLLsLcsp7ypycAkHLUSRT0PAotI5WuNyzBc1Uavv2I1CGzkVYjQgtjx0zUcBopfU8i+tkbifcOyp0FHCworkXlO89SeMldGLldkAj0Ex9EkQ5W+SbKX3gIc8t6oKlsqlDAbOSwVJMt9TopughW/YADmmAm7wA8y2ZIlxTWFEf5sCGdFF0wONLAQzedm8grjzc2UHj5TPTMLIqumIFERUayUc3t2Gsmtaiv/nwala/+wVec/u1Ecs++lehnbySUWwtGLaMB3/iujYVbtSIwxgP2DRGilDcZ46FD+pF10ghKf3M9anouBRdNZdvjNyV5fqySLym6/nG6Xv8obmMlnhmHcA6qrMdafWui73e66Ndsf+lJModeTNXKxRQOv4tnZo1n+B2LcWU+qqrguoH6asD+S6MZw5UeqUYETdEBiNvxDm5VwB7FrE0IuDqOTcHlM/Eaa1CMCAgFIzsN652bE/OaMWQGKT2PYftfZjdVUpmYqKRS+Ms5AIlD905X3k8jKQTrecABjyLIO/0KnI9nE28eC4OmYpEGRiZufSXQVOrsoql4joUSTiNFxvhZJwsznIMrCdb9gAOWwCDvABRD57MdcfoXhjEUgeVJzJrKhDHe4+jjSc3JxNBVPLMaVfdA1REhDevLPycrT66dRnr/0dSv/StOdSlqSkbic4JwtoD9AcWzEwZ3zv+OoeL3d6Km55Jz+vW40do2YZhqei6qW4X9xdNo3c9ATc9ChNOw1j2J3n8sQk9H2vU43/yB/HNupuyZKbj1lXi1pZw/egKO67G2OMbQLmHchsC4Cdh/abT8/N+QFkJXtKbHgpD1gwnXtagq2Uz3AccTzsxBExae5uHWVaFm5mOtmYII57TMbfEqRHoW6ced3SZnvPy3Eym6+kGyTroCNbMTemYBdZWNHfwNAwJ+OKomsNbMTuxvRTgHadVipKUj1UzyL5yKUATSkwjVw0iLYFVsZsdLS3DrKyn85RyKw4dQkGoE637AAUlgkHcEQlDW4FLW0LLxGpimk9O5Oxn5nRg1axHy/duxwjnoR43G/uYFtF4XIKSD0W8Msvcl2J8+gle9ARkrJ9ylN0XXPkrtO8/iRusS7xmEswXsD3iKTuZPriDrR+cgQil0u2EJ4GGVb8ZzFLTsItT0XLJOuhItqxA9Mxv700XovYZjfTgXEc7BOPYOjKNGI81KpPQQRhZGv9F4IoRbX5moy+vWV5J3zZIgnyzggKA+7htTESOE1jRXBznkBxeqapDTuTtX3vM4OnVY701JeMO1oXPx8gZiHH4p0qxFejZCCyGkScqhfQnfsAQcG2tHBVUrH8bcsh7PjoNZg6rlg1WDphpB+lnAAYumClJDFkK6CWNcye6D1u8GvwRqfAcShXBRF6TnIoTAWv8I8Y9XISKFFF02Dbu2gerXfsMhPx+PZ+nYajgoBRhwwBFYax2BlKTowjcamiiVmYxc+CKajKJ8Ng8ZK8c47g7sr36PdvjlCNfEXDWhJazt2MnYny1BxqvwGrYRys0m99zxNKz388sSOeRBOFtAB+PqqWQPPRN720r0ridjrp2e6MehwdMpGvMbvLodVL/2OJknXoxnCLTuZ/jGeN5A9J7nYK66raXvD5yE/ekjyHgVxpAZdB6zDLtuO9Bcz9cfX8ig3wfs3zR7yA1VR28KWQ885AcZoUxufOJ1sjIMrPWPJEX5eHYDes9zMVdN9L3kfa7F+mB2q7luAvbG59G7X0DeBXcCAiMtjIx4yIZvMTe/RPoRV1GvdgqM8oADDk0VpKtViHgNnh1DRAr9En99rkG4JtYnCxLjwnzn1uRxYVXjVW/AWjsNfcA48n56GQgoe+TqhH5SYJQHHEgEKusdgGfZHN854hsNQIouODIvTHrhYRR07e7XYs7ugwhloXU/A6w6rA9nJ4eqfzALve+1GENm4JS8gbV2Kl7lt6Qefjw97vwr3W54FDUzJxC5COhwwqIB+4vlGD1/htVkjENzysVUNNVDz8wg/9ybiX6xGjUlC2Fk+Z7xwy9te82Hs9F6X+rfXjMFRbUx0tPJu+BOUvqehKooDOkcwY5ZHfm1AwK+k0azKWRdNwjpRtJjAQc+qqqgSYWMzHQ8xUA/8krsdYsw37kFe90iFCMTa+003wjpfWnbdf7DuWjdz8BZNwc9JYxu2JirxmO+fRP2ukVNh/UNZBp1ZEbiaEFN8oADiFQjhrCrsL/6I0LRCP1oDsbxM1EiBYmxsMtx0fvSxH2hhXHWz0PRREI/KSgHGHCgEXjI9zGqquDUOaz/+9ccdWo3wrkhwqk6rm1h2R7oih/K1vtSvIZihJEFrUJ5mpGxckQ4Hw/Qel2EtfUl1PQchFuDtXpO4iQxffDdwel5QIcipIPW/Qw/JLOdfiztWmR8B8LIIuekC3FcDy2Uid732l1eI/T0lttI7M+XoB9+Bbk/vw1UBRk3dyvsoqqCuGv7n6UqhFU9OE0P2OfUNxnfET2cCFlvCAzygwJdV4iaFk7cJkWPoSgK5popLTmyh5wFUrbc19N3Odf5j3tY77ccTopwju9F/HB2sN4HHJAo0oFIAVqfXyGaNDSU1CJAfOe4UNJ7YAy9H6HoCCMdEc4BXIquWYQUCqq0SFMhRkqwtgccEAQe8n2MJgVvP7ae4k928OaCD3l52ru8OnMtokl7zUPFGDgJYWThfLkcEcpEOvFEPcZmEvftehQ9gogUouhhnHVzkk4S7bV3kWoEIZABHYNff1dFGFlIq6bdfiz0VN9r9PZNmKvGo8ooVvXXiHDeLq+Rdn3iNoDW6wKs96ehGgo4FhEa/c/eRZtKamoZfscihl53H8PvWERJTe0uXx8QsLdo9oaHtRBh3S97FuSQH/ioqqC4qobhdy2ivGYbRMuSDheVbmegdzoer2FrYg6Tdv0u5zr/cZFkmLTnOQzW+4ADCU9oSCWMkDbm6knEX7sSc/UkEOI7x4XXUIxAYH/6CObqyeh9rkW6ku1/vgc1lEr1a49TsWQkkVhxsLbv57z//vucfvrpHfb5RxxxBJs3b97n1+5MYJDvazxorGpRgMztkc6Pf9WVlFAd6ZEYDjrSyEQYGch4FdaXz0JKJ99Ib5qURKQQ47g78eJVKEYmSA/t6ElIs7H9k8RAaT2gA1BVQSRWTN1H/w8RysbZ/BLGwAnJ/XjwVKz1jyWHo62ZgpHTB6+hGGfHeowh05OvGTgJ56tnm0oETcOLVyNSOiFj5bj1FWydcx4VS0aSEitGTdHRDTWpXXHX5ppZT7CtohqAbRXVXDPrCeKuvQ9/nYAAaDB9UbewHsJQm3PIA4XgA524P/S47QAAIABJREFUazOyaY45tDALoYXBNRPzmHH4ZdifLwfVwBgyDREpxPnq2bbr/MAJ/rw5ZAZSUf1w3uw+/vO78hwG633AAUKjFUGRNvbnT6P3H0voxPno/cdiffMixpAZux8XXy5PTl/7cDZC1fxqBM9MIvvkK1HTc6l4ejwpIog62huceuqpDBgwgIEDByb+lZeXf/eFOzFo0CBWrlz5g9qybds2jjjiCBznwJ3/gpD1fY0CqTlhGqvi5PZI56QrMpDrJmGta1JdPWEuzoZlaL0uQD9hJkTLQUsFPYXQifMBDyR4bhw8G5Agofqtv5Ix8KcJUYxmRKQwUFoP6BAiRKl5ZQmZJ15M3Udvkd7vSuytrxI68QHwHFA0PNfGK1+VdJ2MlfsyhEY6Rs+fY337N0In3u8/KVQkAuPYX4NQ8Jw4ODVI10QpHIpbVwU0lQh6ejwFIxdTJrLJCWtYcX+idlwvYYw3s62iGsf10JNt94CAvUqDGUUgMDQdz5MYmh7kkB8EtJ5jMtNSkPFG0FJ9QcqNz+EpYYx+o5uU1SWhofMAkKpBaNgCpFWLUENIJeRXWvn0MbzyVSiFQzGOnYy06hF6Gkrh0KT5M1jvA/ZnfE0FAR6ggINEStB7Dcfe+Dxa9zP8Cio9f4anpWEcOwkRysZTUwgNW+DveaWL9dUf8Ko3oGT3QUnvSejE+X7UnFAIHdIPc8t63MZqsk66kvKnJ+C5TlCffC/x6KOPMnTo0I5uxkFB4CHfxzhCMmx0P1Jzwgw8Ow+5blqSd1Cxa/DKV+FUrkd4Ns6mvyEat2C9dRPxVy7DfOc2pFOP0CIQygahYn37V3J+ehWeNNCO3ukkccgMEGog9hKwz1E8O1FLt/Jv87BNBb3LyZjv3OqHpr1zK8IzUQ79BcaQGYROnI8x9H6Mk5eBXY/9/t1Iqxat0/FIux5r3WJkYzHW2zcTf/UK/3ppgZYCThy9/42IUDqhQ/oBTYrrjZW4DdXUOf5mAEBTFboWZCe1tWtBNpoaTIcB+5YGM0qKEUY0yW8aqkHcMTu4VQE/lKQ5RtFQInnY70/H3voy2tG3IKQHCAjnIBQFc9V44q9egfXWTcjGUqTnYn26FGnFsVZP9I3x7D7ovYZjrpqI+dYNmKsnoR85AqXQ3wyLSCH64LuJuSmkqTEyRB1paiwI1w3oMFRVJPqiU1eJYnm8Pv8jVkx9l9fnf4RsdBFC+sb44ZeD0EG6eA0l4MRAS8X6dCmicSvm2+OIvzrCD08/9HyM//kD+oBxvshhk0gidi2drpxJl1ELkY6DmpKBll3ExmoLSwnW971NbW0to0eP5oQTTmDw4MGMHj2asrKyxPM1NTXcfvvtDBs2jMGDB3P99dcD8N577/GTn/wk8bry8nJuvPFGTjjhBE499VSWL1+eeO6TTz5h+PDhHHvssQwdOpSZM2d+Z7s++eQTLr74YgYNGsSwYcOYMWMGlpUs+vvGG29w2mmncfzxxzN79mw8r+Xw5rnnnuPMM89k8ODBjBw5kuLi4nY/54033uCss85i4MCB/PjHP2bZsmX/2Q/XRNBD9zGu6yFSVU655RhyuoXbhpxZNShH3YDR9TSsNVMS5Z+SQ3qnIRAICZ5iYBx6Loqmomgetf9+A2PofMKnLUcfMA77kwW474wlXZQFRnnAPsVTdNS07KZSZKDpSpIoUSI8vefPsdctwt6yEowsFFXDahI/kmYVSkonrDXTdjMWXIQbRwoVLezQecQMuoxZTMpRJ+E2VHNIukDBSyzIYVXnN5OvTmyYuxZk85vJVxNuChkOCNhXNJhR0kIpyKYSfYaqYQYG+QFDa4OjtfGbahi8MucaPnhkDIpn4TlxRN5AtN6XgeeCXeuXMa37BmvN1DZVJLDq0PuOwrPjief8nPGd57+pGEeNInzacrShC2lUijAatlGxZCTb5p4b5NAGdBjNKWvNfbF48VWoDVsJZ/gRHI1Vcd5+bL2fctnrAtipJpAixG72wFNQpAtOY5OYW8vjIlqC3PgQRmYYhELGJXPQNY0stzo4oNrLeJ7H8OHDef3113n99dcJhULMmDEj8fzEiROJxWL8/e9/Z9WqVVx11VXtvseYMWM44ogjePPNN3nqqad46qmneOuttwC49957GTFiBB988AH/+te/OPPMM7+zXYqicPvtt/Puu+/y+9//ntWrV/O73/0u6TX/+te/+POf/8wLL7zAa6+9xp///GcAXnnlFR577DEefvhhVq9ezXHHHcdtt93W7ufccccdzJgxgw8//JAVK1Zwwgkn/Kc/nd/O7/XqgD2C63qYnosntDZiFc6O9RhFP0KaVTsprLYgY+XI+A6Ilvgl0dYtIv7qFcgv55P9ozNxGqoxV03Aeu92vOoNgdhLQIcQIwU1PQ8tuwgAaUXb78tmNSJvIMbhl0OsIkn8yCldBdLb7VgQoVzssncRVjX2JwuIv3YV8sv5FJxzHU5NOfb2TeQ0bEFXfKPHdSWdszJ5/t6xrF16JyumX0UnwwYzEHYL2Lc0mlFSQhGaNYANzSBuBwb5gcDOBkez8RsJK6R6JahrbiFtzSjMVbf5lSb6jgItBUXaiQPHXc5pWhgAJZTaEvG2q72A6a/3EjDcRiqeHp84BA1KQAV0FBGibfpi1XOTOPr0vMRrGqvivuhrShHCjWN/ssD3dn+y4DvXfb/872z0vqMSugp+tYE8f8/74b2oOQWEwwaRP4yheM45wQHVXmDs2LEMGjSIQYMGcccdd3D66acTiURIS0tjzJgxrF27FoCKigrefPNNpk+fTmZmJrquM2TIkDbvt27dOqqqqrjhhhswDINu3brxi1/8gn/84x8AaJrGli1bqKqqIjU1lWOOOeY729ivXz+OOeYYNE2ja9euXHzxxYl2NXPttdeSlZVF586dGTFiBCtWrADg97//PaNGjaJXr15omsZ1113Hhg0b2vWSa5rG119/TUNDA5mZmRx11FHf67cMko32IaqqoEcMEAKkxLMstP4TE8roIlKIfui5fpkzz8YYeh8iNQtj6H3YG5Yjqz8HmpRXrRrsdYvQB4zzTxDLVyXqOhsnzMEMxF4C9jI792c7ZiXlaLmuxAunUnjVAmgsR0nJaFfjANfEOPxS3zDXwglldRkrRysaCsgktWEZK0dkH4l+xKWIcBZooB96DtY7t/n9PLuPX6PUNckYeDI1762gfs2LFI5aQj1pibaFVZ1oxdcsufk8qko2k9O5OyMf/AspBYcFZVIC9gkNZiOpRouHXFc1TMf6jqsC9gfaMzgqnh5Pl7FLcL54Cr3/WN+YkB54jSikgKLimbEWb1+rOa0ZESlEOnGUiIGUCsaQ6b4XfVevtesT67v0vER7mnGqS4O1P2Cfo3h2u30xM7XFGE7NCeMqYVQ3hrXxueQxo6mETn4QtHSUTkPxypK1Epr7vfXBLF8Ibs0Ufz8hfD+jjJWjKlD65Lg2Y7Rg1DIaiOyDX+HgZ9GiRYkc8lgsxpQpU3jrrbeora0FoLGxEdd1KSsrIzMzk8zMzN2+X3FxMRUVFQwaNCjxmOu6ifv33nsvCxcu5Mwzz6Rr167ccMMNnHLKKbt9z2+//ZZZs2axfv16YrEYruu2MZaLiooSt7t06UJFRQUAJSUl3HfffcyePTvxvJSS8vJyunTpkvQeCxcu5JFHHuH+++/niCOO4LbbbmPgwIG7bVtrAoN8L9JawELRBXZIZ1VxjJAK/dJihBSL8ucfpNNlM0E4CF1DqB6SECKsY26djSwpQ+idCB09CevjpRCvxhg4AXvDMt8wMdKbxN18ZKwcoSiB2EvAXkVVFUQ4xKriGFFbkqILhnSOoO5U/9v0DNJCLtYnD2EX57RsLpsOoIzBU5GofgqGkQmKhtQzCA2dg7X+MYSejhfbnhBD0gdNAWxEOIS5aSrSbhofPWZAOBslnIPeZ2QixE1ECskcPI3ohrcRntPUdoHpOihmA8uajHGAqpLNLLv5PG586h1QMxLfU5OCxqoYIUXFETIQhgnYYzSYMVJD4cT9wEO+/6GqCrE6kxCqL0TVNAfsyuCAJpGqb55HO2w4IiU1ea7qPi1hYDhfPYtx7GSsD2a1zIkDJyFD2VjrHkbvdSH21pfRB4xDpHfHGDwNa+20Vq/19wLN67unSLTsoqR2adlFwdofsM/xFL3dvmiZTWkdOWGGje6HKyWKUFqE3XpdgEhJIf7NuJYx038GdlpXvK//mNTvgYQXvVkzyYvtAJoO+xWl3TEqZCDytjd4/PHH+fbbb/njH/9Ifn4+GzZs4LzzzkNKSadOnaitraWuro6MjIxdvkdRURFdu3bl5Zdfbvf5Hj168MADD+B5Hi+//DI33XQT77333m7bNW3aNPr27cv9999PWloaTz75ZBtV99LSUnr37g34RnhBQUGiPddddx3nnHPOd37/AQMG8Mgjj2DbNs888wzjxo3jjTfe+M7rmglm6b2EqirIRpfXH1tPY1Wck8cfxzrPIaTCsVoJ1cvGEz77Vtz6SqpeXU7umRdjbr4TaZcR6jkLa9sCpO2LIUi7DLN4NuEhD+BVf4O9YRle9YamGs5peA1bEp/r12fchn7EFdiAV74qIfZSb0WAwPMX8MMxUkK8s803xgGitmRNSYyhXcK4DS1lmyJqFOu9qYkQMxnK9asFSBukh1XzDXp6Vz/kstUm09r4PPqRIxBGBvbGv6AdcjraYRcjwirSqcXcdF/y+Ng0BaPPOHCctvlma6dRNGIeCJWIp7B5Rw0jZz3B87f/ImGMN1NVshnPNkFtO4abNxBqqhos5AF7hAazkfz0FoFBQ9WI24GHfH+heQ54+bGP28wBUrRvcAgB9jfPox95PlJYmJvmJM9Vm6cRHnA/Zu1GvOoN2F//ya88gQQUpKKBVY9XvgqrbqPv/Xvvdn+9P3w84vBbCOUW4TWW+Afz8arE+q6ogqJrH8Gt34HbUE39v1eQ9dNRxEghWPsD9iUxUij81ULcqmIUI4JnxVBzuuIYBZw9vSBxuGXIepDSX/N7DUdiYm6e3WZ9Dx92P7LH+RArxf5sKV71BsDf84pIIcaJDyCFivPJgsTBlu147Y7Rr6os9HSPAiPI2t2TNDY2EgqFyMjIoKamhocffjjxXEFBAT/5yU+YPn06U6ZMISUlhY8++ojBgwcnvceAAQNITU1lyZIljBgxAl3X2bhxI/F4nAEDBvDiiy/y4x//mJycnIRhr7QS7LMsC9d1E/d1XaexsZHU1FRSU1PZuHEjzz77LDk5OUmfu2zZMo4++mii0SjLly/n6quvBuCSSy7hwQcfpE+fPvTu3Zv6+nrefvvtNrnrlmXx0ksvccopp5Cenk5qampSu/4TAoN8L6FJkdjIA4QyDKLbbQZmxKh+3A9zq3njKfIvvAuhOZibpyYmIKGEE7eb8e972OsWJXsXFQNn80v+da1ODmW8CmPILOh7Ha4Sot6K4ARhuAF7AFVVkEIkjPFmorb0w9cBTRWkGjE0LJT+Y3G+ehYAYVZhNudPRgoJDZ2HuWr8TqJGc5tC0Kai/2guerfTsDcsQ+9/DeamyYQOmdzu+FDSuyIba9rNN8PcgfXBLEKDpvHiW5+zraIaRVHJ6dw9ySjP6dwdRVVxaTuGm0VoTrnlGFwCAn44jWaUiN7KQ64aRO1A62N/YXdzAKpKwUVTqfjTdJzqUrTsIgoumor0HLQep2MWz93lXCWdej8818hChLKxSlejZx3qV46IFGIcOxklu49vdITz/ZrMejoYOZQ+fRcAeWeMwjj2Dhx06i1fh8Bo2EZpUxi9ll1E4RXzMNO64lrBAWJAB2Db7PjL7Fb98X4szcNF+qXPAA8NgYtx1LVIqx4lJRdZ0s6YsatAy0OmHoLe7wbwLKQTh5ROfjRJ718gwgXofUchnTgeYeortlNw+RwqnpmYaEPoolnMeauKHdFynrhsQAf8KAcvV155JePHj+eEE06goKCAq6++mldeeSXx/Jw5c5g5cyZnnnkmtm1z/PHHtzHIVVXl0UcfZfbs2Zx22mlYlkXPnj0ZN24cAG+99RazZs0iHo/TuXNn5s+fTzjcsobuHCL+xBNPMGnSJO666y6WLVtGnz59OOuss3j33XeTXnfaaacxfPhwGhoaOP/887nwwgsB+J//+R8aGxu59dZbKS4uJj09naFDh7YrJvfiiy9y991347ouPXv2ZO7cud/r9wsM8r2FR2IRB9BDKp3SPDJUl/qm0zpzy3qqVi6m6MppxDe2TEDSrUfonZIWcqF3QkrRVIvR8WswS1CEgtH/BuRhFyHt+oT3HEBoOpYbImqFOjQnVlUFmLW4roWqGhDKDHJ0D2D0iEGj7ZGiC0KqwuF5BoYicKREQaKpgnRRhr3qLpxWXm+phhNiRtAiSNSuAR3OxzjuDoQQePEqMDIQivAX5l2Nj3g1wsjcbZ6l8/40rj9rNotfeJNtVY1cds8T/O7OqxM55Jfd8wSqFvYN7p3GMDTdD/a2AXuIuGNiaC3q/oamUxWt6cAWBSSxmzlA2HF2vPQwuWffipqSgRuto/Klh+k0YiYinLXbuUpoaUh2YH/6iH94fsx4mlWmE3mxAycihEDoqXhmDfZnS5DxKgrPH8+OV37nlz0NF1BbHQMkaWqsTU57eVO+rBXkywbsYyJEKX/6tp36421t8reFAMwazKZUDGPofe2v724NQuoIR2C9P6PFMTVkBljVWP+e6dcj17OwK7+l8pVHyB5+Fy8/+yj/e+1jVEdtwrpKdcxm1ECdxz5swPGCfegP4bXXXku6X1hYyNNPP5302CWXXJK4nZWVlZSL3czxxx/Pm2++mfQ+DzzwQLufOW/evHYf79q1K1988cUu2/rSSy8l3b/55psTt5uvGzFiRLvXnnfeeZx33nntPtf6M79vmbOd2e8M8tmzZ7Ny5UqKi4v529/+xuGHHw74SfmTJ0+mpqYm8Uft0aNHxzZ2dyh+jkzzYi5tlyPzwjQ0NCaF0Jhb1mPtKElMQEpKX1BSCfWYvlOO7N1Iy8J6b3zSRCRD2UihYTfloDUjIoWYxV9S8cL9FF5xP9FI5w4xglVVEK34OpGrGwhnHQQIwefbTQZ3juBK+KC0VR55lwipRhx71V1tvN6hEx9ot8yfiBQiwjlovS/1BV1QEID173tbRYNMQ8aqEXonnNrVhHpMw9w0rWV8dJmEtBzsT2ZjDJyE9eHsNnmWzW2J6P7G9w9vfcqtZx3NRXcuxoikYsUaSc/pBEYaqgcxRTBk4nE49Rab/rmZqk11pOaE/doUgVEesAeI2xZGq3J7uqoHom77Ezut40BiDvCEjltfSfnTExLPadlFOLbAiOTsdq4y196XpAcjUgpBur5BYdfjlK5C6ClYa9rmizvr51EwfD4NbjphrWULt6uc9kDQLaAj+E/7o/CchDEOYG9YTmjQDMxNU1rGzCG3Y1X8CaNoDNbqnSLq1kxJiLpJ18Gq3EHJ4+PRsouoLN3Ka0/M48RfXI/dUMPScecn9qF3zPkzhqqwo8EK8skD9gv2uwSK0047jWeeeaaNet3UqVO57LLLWLlyJZdddhlTpkzpoBb+ZzhCMmx0P3/xBoShsaY4yvqGCPlXzEuUgtKyiyBShNHjbpT0YRidRmJtnYNV/CBG13FEjlhOqGgyiCzs937dZiIith1h7kAfen9LeZRIIVq/8VS+8mTiVDJFdFDZE7O2XeEszNqOac9uUFWB6tSBuQPVqQtKY+wKKTFdD8drMcahKY+8OIYqnPa93k1q6c0o2X0QehrGifPR+9+Es/klpF2PEs5OCBc1X2utnYZ0JUa3KejZ/4NV9luMLmMJ93qI8KFzsb/5F0Iofk7mhqWEhs4lfOoT6APGJUeNRAoRisbKu0dwy7lDWPPiEziWL6LlWCYrHrwdz6ylwvK46nefcMGzH3PLW9/Q+cJedB6Qy7DR/XBEcJAU8MNxPRfHc9DVFqMqpOmYQQ75fsPO63hzDrkjJDFSKNhpLS+44n5e+NrClSGMHjN3OVfJ6s8TB5XGwEmAgjSrsT9bgr1uEcbhlyaMcaDVayciwjlIKbF2CkNvFtFqTSDoFtBR7Ko/Cjx/r6UK0tQY4LXdL4gQRtdxhHstwOg6DilC6Pm/BGnsovRpTtPaLpCeTKSPCEXl1KvHkxYJUZCTzS3L/sUJw39FVclm/jjxArZXlHLF8g+psDxUdb8zhwL+j7HfzdStpe6bqays5LPPPuOJJ54A4Oyzz+buu++mqqqqTWL+/oLreqipqp9r5oGiKURtSdSGHakFlJ02jfyMMPW2JN20EY5HqOsNxL8e15QvU4b57eQm7/gshLDQj74e+4tnE+XPmlXWrfWPYvS/oUnspQtmyVeUv/AQ5pb1QJPyq2dDB4Stua7VvnCWa+9XvS/w5P/n2DGLIZ0jeNBuHrkndN/wDmf7pclCGUg7hlSMhPda5A1E73ku1tpp6P3H4lSuxxgwCulGQVMhnA2tFl4ZK0cIBWlFMcvn+eOj/m3AD2czuo5D2vX+a+NVoGh40kBJ64p+1BikVYOz+SX0I6/EQaF3t2xsy2bIOVfy7LRr2PxJi0rnubfN5bG1lZTU+oZ6Sa3JpJe+5MnLBiA8LzhJD9gjNBveeisvp67qxB2zuZJgMtJLlPQJ2Dc0r+P/O/E4bNNNUlkHiEW6UDBqmV9yTAuhaJLTIzYqDlIJYW6a3WauCh05B3nITxGqijTr8DwX6/9dlawBY9bu4lBToPW7EamEyIzE8aJRMiMKjVYkcUBQ0SqHvOCKeYGgW0CHECOFwivmUd6qP+ZfeBeVK+aTe94kvIZqyp8eT9cblrTsF476FUpaF+Ibb2kTsm4UjkeE2y+dKox0Xwx2/SMY/W6ky6iFSCtKp7wiTr/sOsqXjk604dyRswB49/nHydMdclN0bnn+M564bABqsLYHdCD7kUm0a0pLSyksLERVVcBP+i8oKKC0tPR7GeS5uWl7q4kA5Oen7/K5uNOSc6sqkpuX/pOCrHSeuW04IV1BiTjgme2LuXn1xL66san82QSsj5chqz/3J6b4DrTuZyARCFUDAUIzkt6j+ZR8d+3bE7T3/nU7GtsVzjLCYTLy9mx7fsj3q9tR1q4n/5Zn3iUnv9Ne/exdsbf7azPS83DqK8nSLISqg6oirThCM1BTsxHtKEVKKYk5fph6s1GeHRYMyIujqC6hnzyAZ9dibW0VqpkyA2lkoh/7a0SkAFm/CePYyRApQM/IJ75pYstrm8r8NR8+tdQhT9+loJu1ZmYivN2zYwjPSoTCJVI8tFS8VeP8ur2RQvKOm8pFv36YeZf4wiI5nbuzfctX/PKoI3jtq+rEZ5TUmnhCkJOTurf+DN+bvT2evy97ur/ub99vT1PZ4OeKp6ekkJWZAkBGagqWY5Obm4YQLRE6dtU//aipLjej57QVk9nf2R//lt+3v+76ODsNKT1k3bdY654gs9dwzLfnYgxpX9BNCIFIzceqeBav4QNCPWagHHIG3paXEoKWzak8OxseXmMpQg0hhIP1zs0JjY6MITMQ6T0hsxfdblzmV7EQOoRziKgdt83bH//uzezPbWuP79Nf95fvZtdmJ2ksVK1cjLllPblnj0sY6rYLxokPIpUo2GUgnfbHjRYCobZNSRs8DSl0pJqCdsQVCKcO6/0W4Vit/0TU9Fyc6lKc6lK2/24yP7v2Ab5891Uqt37F6IGHccM/ypBC7De/W2v2xzYF7B0OCIN8T1FZ2YC3l0Qc8vPT2b69fpfPG4bKjw+JYLmCDdtjPH3XtWRbFVQ9cT2FV81E1W08abYvAKPnET5sMdKpxKp8Af2IS7E/XuzXK5USEc4Duw5vw1ziTZNQ4fCJlD//IG59JeHh99BAmMbdtK89vo8Y266+v6qmMfLBv7TxPDtK2m5/r+/Ld/3+34kZa9eTb8Xj3/m+O3/2nppA92Z/bUZVBZFYcbJX5aKpVL70MG59pe9hiXRp9++uqgpDOkdYUxIjpMIJ+eV4W+4k3mxUd5uE0HNaIj42TyHcawGEDCQuGBFQJAgLq+zpNmX+jD7jsFb9usmYno79+XK0nqe3L/gidPSjxiBCOXh2I8KswvpoXttcswHjkh5z/j2dwhMeoPuA46nfUcal05ex4qE7+OXs39O/czrrSvy/a+fMEELKPdpnfwg/uL/v9F57gj3ZX/fk99tfsTQ/AsO1JTW1fkqR54BEUlJehaE2HaxKSXj771C8OOa2h6lxTkxUMzgQ2B/7Kny//vpd3yEzYuK+dyd6/7F+ybOjr0eEc9qdqzxzE1bxIkI9ZmAD5qYphA6fhbnlpURNZfvrP2EMno61dmqbHHIZryI0dG7buW3oAnDqsdfclbhGH3w3tbJTh1RY2Z/H8L5qW0f01/3pd09ToXLFA0m55Cl9TwLpR26GDumHLhuQZgyhu5jbFmB0GdvuuMHIAQVEWg7GsDlIMwpmDRiZyPqmvZu0sZoqEUHTGr9uDrk/HU3lK0+S+9OrUNMyUTLSuHreH3lu1k38751P7XfrezP7+m8ZGP8dywER/1ZUVER5eXmitpzrulRUVFBUVPQdV+4fGIaKCBm4UvBecZS4C0WpgsY//RqnuhQjOxdz62zs8uWEuk3wJx+awtt6zMAsXkT86+uxihdh5J+PyDzMX/g3LEUoOsLIaqNe7aybQ+5l91B1xj00pHRCQ01q03flSzeHcD901Yncc+ahPHTViUQrvv7eedWuK0kpOIwbn3yHu/75LTc++c5+GQauqgY5nbsnPeaXwNJ3ccXBQYRoG2Xeij9NJ+ukK/3bT48nQvv6A67rIeMmw7qGGdbFwttyZ7JRvXU2esGlidf7hvl2Yl9cjvnNrQjdxdr+GOamSRj55/uChq1eq6QVETpxPqGhc7C/+QtG/7EQ6U6ox4w2Y0RKFXvLPzBX3QZmle+Bby/XTAtBIbu+AAAgAElEQVS3eUzB5YqZv2XMYyt5/x+/o35HGd/WONx8ck/6d06nc2aIBy88CsMLwtkC9hzN9cY1tWVublZcb51HLuyNKM5WPL0nQjaieMlCSQEdi6oqSGknqkPoR56PVbkIc+vMpkPJ1nPVdFBSEXoO5qYp6IWXIfQchOL3AREpRDpx9F4XIvVUjGELCZ/2FKEfzUrkMMhYOcjkwosyVo6QdsIYb37MXnsXqUZQRi+gY2hPZyH3rJvAc9Cyi8j72fVYa6agpGRjbf8zRpexoOUT6jFtp3FzN1JaxDfeTOzLEZibJiBCAlQDEDhfLkdoYV8Utp11X83uROH5N8Lmx3Devw179a3kZKtk5BXR6KrMH943WN8DOpwDwkOem5tLnz59WLFiBeeeey4rVqygT58++23+eGtUVUGNhGiwPHTFz7HtmalR0xBtOTWUHkLPQS+4FKmkEjp0FngWQsvA3PYwXlP+mW/kzCXccw7mmil+7kwoBymUdiehsKHQ97Ai4m5qkgDMzvnS/U89j19OW4gW0fDQaLQiyF2Isd345DugZXyv38B1pX+Nhl9Oaj8zxgHUSBYjF7zAslYqnCMf/AuEMvfL9u4pdqWEqqZkJG7vTqVXVUEXdQgvjtFlLHbFs3jRz4CmMDO15cS1uXRJ83Pm1tkYXcZibroLc+vcxO3m13p1W7A/XuxvRhs2Y219Bb3HT7FKlvin6Go60q3HKluOnnc2+hHnIXuci6Kn4MXbhnwqhUMRoewWJeOvnkXGqzDjJvf8rHei7NmpV09gKzk8s3YbDwzvg+f5ouqWlBgQ5JAH7BHitu8h15SWZbhZcT1mm6SH/RBV1fLHk5tyGkrtb1CsL/EinfdxawN2haUolFXa9IwUIkIpieoo0i7DKluK0XUcSqgbnrkVq/hBpF1FqMd0pNsIUkUvHIH03Ja0GrsBe8NSMLLRj7wS6/On0LqfgTCyMI6djPXt35FmdVIbRKQQPLfdfcDO87emClKNGIp08IS/3neEBz3g4Md1ZUJnQcPGqvgWL1YHikr+hXeh5xRhxsqRqBgFF2FumdmUgjaM8KFz/fK+5lZQM7E23pR84L9pCuGec/HsOGT3T2jItKna4sQR4VSsd6cnHVapn83jl9MWEjMKkKYTrOsBHc5+Z5Dfc889vPzyy+zYsYOrr76arKws/v73vzNt2jQmT57M4sWLycjIaLeW3f6IHjEwHYkmBKbr59wqQlIeleRlF6Gm5+JhYHS6FnPr7EQObfjQuUhre8IYb8afkERLrqxiIOLb2803Q7rI9YtJPfIqhFFEWI36izCC5x+ZTlXJZnocfTy/uGUScu1ErKYwt/TBd1OvhHcpxqaG2oayH8ioqqC+7CteemQ650+cT1pOARl5ndDTO2HZB+8kraoCIVQ6j/kNbkM1NW88hbllPVp2EW60Dti9Sq+mClL1UqyNd2InwtQnYJUtw4t+5hvgnl8uqPVzzbQ22P3bWS2v7TIB+/MXfKGWT5ei9x2F1DMQeHj1bydEkpoRBRdibpqCkTsWc+1ijCHTMU5cgL3uIbCq0fpeh9DCmKsntQoBnYSnZ/Lc/HsAv3//7s6ruWHZ67z1RRUjh3ajstHm1uc/o6TWpHNmiPnD+1JgJJdIUVVB3LVxXA9NVQir+n4XARKw/9GeQa43GeRxx0w8pthfIZVsPO1QJDqKvQEiJ+/TtgbsGseT/PHTKHf+eJYvxtcq1NaLfob57WTCvR7C/HZy4nFz01SMruPAq0eEeiDjMfQB40BPxXpjNADGkBnYnz+F3ms41odzk7QwPE9JrPn+XmA6Tl1l+3nnreZvTRWkq1XQUIrQwihOnPRIEfXkBEZ5wF7BdSUNRMjLzcOJNeDWb0dNzyNUUIRQmwRg8RLGOIBX/zbxb74m1Gse5reTiRz5TLt55dKqQTgS/dDzsb94Cho2ow+agnDNpDzz1ikeietj5ei6RmZEpywaDdbvg5RTTz0VwzAIhUKYpsmgQYOYOnUqGzdu5N5776Wurg7btsnIyODhhx8mLy+vw9q63xnkd955J3feeWebx3v16sWf/vSnDmjRD0QITEeiCNjeYNE/W2LZMUQolYJfLYT6Sry6Cpwds5NO/6RTg3Rr2s+lERqhH83Gkx7Oln9CxbsYQ2Ykwtab882s9Y+hdT8D5/MnST3ySqxVUxIiML+4ZRL1laWce+Nd6F8+2DbM7Ufz2xVjUzS9rRr5ghfIyOi/b3/XPUmraIB1r78I+N/1v4kGOFBozh0v3UkBtfadP5A17FIqX3r4O1V6U8MxrE07h6n7nm6reBFGj3uAdMKHPYVQDcySRQnvOTR7zOsTt4WeT7jX0qZTbYnRdxSeHUU/7CL/lDtSANJtP4fcrfeN+lBGU07lVPQB49CPvBKpGBAtw/rgvp3KCM0mdsTtHPWTsxn88xFEa6t45fHZ1G4v4aLe3dgUdbjv5a+T1NZ3VmNVVUFJTS3XzHqCbRXVdC3I5jeTr6Zz1q71FgICoMUgb132LBGy3qoWuWJ9hTR6IQGp5qM4Jfu0nQHt03wQ51oWIwcXIdQ4Mla6i/mpJulaaZchlLDv5TvsQXAaEWldsba+ipLdB633pShp3TGOuhbr06Vt8sWVPhPQB89CqAKERt3HbxLp3g/t6Ek4H7cYIvrgu6m3IjTP36khCxGtwvpkQdLBZGpKGrXRgzs9K6BjEYpCLNKFtKxCVLsC+7PH0A89n9DQuSBE+0Ju0msKXVfaH1fxGj+KbuhcjMMvw/pgJsJrMcbBHzNeQ3G7h1VxR1BesoPLZyxJWr+75mRhH8TOmP9rLFy4kMMPPxzXdbn88sv517/+xaJFixg/fjynnHIKAJs2bSIS2feVqFpzQOSQH8goimBzjUVYATVey/DbFzLsuvu4fuYSpJ6GwEJNa0c52q3BrlzZbk65V+97yZ2P5uJ98YRf5klPQ+8/ltCJ85vyy5fhla9C6Olo3c9ok2Ouf/kg5954Fxk5ee2HuQnJyAf/ksirbg7hVhWtbSj7uPOpKd18wNbt3m1ptoOU9nLHtz93N7nnTEDk9ST/kvsoGLVst4Juiuq2r3gePpTwoQsQMgOndCOV/3wKzwEj9/zkvtxtEnbFs4kcMatiA9gu5hu3YL5yOebqSQg37tfm/WSBL1wYqyPUZae8zG4TEu8jTd+z35wvbq2ZgrCqEYafW6Zk98EYMiMxTlKz8nhhzi08dPXJvDDnFn4+biaubRFWXPrmh1h6YW9evKovK0b2Y+ywbpTUmkkZDHHXThjjANsqqrlm1hPED+K+E7BnSBjkWosh1Byy3vwcXhTFLUbq3QCQSibCrdy3DQ1og64rlNbUMvyORZx43X18tbXEV4d2Zdv5qfs07MqVSde3PkSUZjXm2zdhrhqPVjAYve8o7HWLiL92Jebqyei9hqNk90lcK2PlaJl5lP1uKtsWj8W1Qc8qZMdf51H+3ALoPprQab9FHbqQ+p0E3TSsNsaK9eFsNCwCAvYGqqoQUlSitTHSRBSNOPbny/3Ij4/mEX91BEiZGDPNNBvioR7TsTa9QqjLTnvhLhP8MsDNmgrSQ+9/EzLUVj/G+XI5+uBpTd543xhXj5vKwhffY3N5ZZv1uzYeQ9cVXF3DVFVcXQvqlO9FPM+joq6KbVVlVNRV4e2lfH7TNDFNk4yMDMrKyigsLEw816NHD1JTO7aKzn7nIT+YMAwVpKRXToiq+gaumfl4YuAXZKVjUI298SFk2vVNHkI/jxwtH9QMjE4jsMqWN+XLZiH0HDwZxil/DaPHzyCtO8bhI1BSOwMCe/NLeOWrEp+fKBVlZLVrdGf1LqSmopzU9sLc0BNibJ5roxoh9Iw84pXF7RqvdTvKyNLCB6RHuVnQrU00gKrj7ua6A5ld5Y5Lz6PeMYDm0nnS96YTRfFsPEXH1VMxjDhIj1DPWdjlyxOeb6F3wnMVaNyE0CNoGQo5p16KUDXsT18g1H8uQlGQCEDFKBjjn3KvewrjqDGYq8a32Szq/cdirZmCtWYa+sCJSDWLUNFkRCQXzyrGKlvm52V2vR3ro8f8djT1fRkrR4RyQfg55DuHf2rHTSUjvxNVJZsTIeuX3/MUKBolO2q4dvaTiZPzpZOuIu20nnj4mwzX9XBcLzGmm9lWUY3jeujJOooBAUkkDHKlPQ+5/5xo8oZLNdf/X8kAa8u+bGbATqiqQtS0GNnqIC4rLQVXKqiKjvXxYowjxiJCGaBnYpd+gNH5Ssz41y1lHbvfgfRswr0eAiWMyD7SL/Ho2UgZ80ummXXYXzybKIVmrZkC+HObXVWGuWU9AG5dBeVPT0i0r+KF+ykYtYwGN0ybyCbptV/fXAbewIA9j6oqKKZHQ20V2WkxSn87gW7XL0brdQES0+/nro1H2Bcw3jSlZYx0m4RUQtgbV+B9/UfsmqGEj16ANLcj4zXJ5X/NakQ4H2vtNIwhs9p4w2W8iqrKRuL5IynscRg7tm3mnzNu50cXTSISSi4TvK2imorqekSuyujnvtxtulrAD8fzPD4v/Yarlv2abVVldM3pxJMj7+PIokNR2im5+99w0003EQqF2LJlC8OGDWPYsGFcd911XH755QwcOJBjjjmGn/3sZ/Tq1WuPfN5/S2CQ70W0SIh6y+PbaosuRsvG/djDD+HJiRdgr53gh4h/8SzGwCkITOza99BzTsYqWYyefx6hzteCEsKza7C23OsbHj2nY5WuRT/0/OQw9SEzkIddjPPZo8h4lV8mZePzGP3GtBuuY6RkoacrqL2m4H4wI/E+YtDd2FoGbsxEDWWhhkOsKYkRrYxxXLrervHaUFVBRn6XA7NHhTLbLc12MAu6eYqOll2UZJS3ly++c1m0lKNOouCC0ZgbkxdOq2wp0q7C6D4TvHoI4Zc0UwDhIuOVaL0vh1g15r9n+lEcrcqTAMjDLkncbw7bFHo6IpyDkt0Hr3oDQijYG/+EfsQVeNJFGEWEut2OxEM2LZStSwSJSCFSDUG0BKPfGLyGbYhwDjJWnih7du6Nd/HgNWcD/uFSZkFnZDiDVGnyh+ljcD2PsqpaFvzxZWZccz5zX93I5J/2QnU9VFWha0F2klHetSA7OE0P+E6aVdZ1ta2HPGb52guKUwyAVLL9/9VMhGxEyDhSJFcMCNg3aFLQUBtPGvNpKSEcD9RwLnqfEQg9Aq6N9KLoRSciiWJ0HYdQwkgvjlTSsMuXgVuFXjiC0JDJyHgt0kjD2nRvy9x69ASsj5chjCZ9jUghWr/xlL/wkN+W7CLUtHz///Rc8s+5CT2rECk80l2LqBtKinBy0dvdC7gE4eoBex5dCHaUfYmwKqlZs5KC829DqgYiNRWr7M/ouadDuBPCq0p2PmlZeK6F8+kTaJ2GIn56HkJYgIPQs7C3voPe+zKEkYUIZfnRjEI0RcZp6Mfcjv3RzMSe1jnyVjQth5rNm3lu3iVs/uQ9AH50zTTKzeTokK4F2VTWNqAYkd2mqwXsGXY01CSMcYBtVWVctezXrBj3KAUZe0a4uzlk3TRNbrzxRp588kmuvfZazjnnHN59911Wr17NBRdcwNKlSxk8ePAe+cz/hgPRfDpgkBI+327RtyCEHXfoWpBNQVY6E395FrFYI+FmT2D152BFsWPvY+Sfi3RqCXUehVmyBK/+7ZZyKeDXdXarMbodj1e3FZE/EL3TUD/vNl4FWgr6gJv5/+ydd4BU1d3+P+fWme07yxaWpQhWBBONaIJGY4nxl8QgWNFYAMUC6II0o0izURQEiVEBC5aoEdRoXn1fW4waBYOJYJfetrC9za3n98edubvDroq+aF7NPP+we5m7987Mueecb3meByMf6btoB4/Gbm5DGzARd/389srgkTN4eOYk1r38FANPPJ3zZywCVaPF01hfH+FHGUFAYUR1amsqOVS3cU2DKpnLyIWrWN5BjXz4zGW89shihl171D6tKH8VH/T/DTpas/meE1idfUPX+r+CpB1Jiv94F3zxPVvbcwefhrXlhk72ZmafeUhfgmIivErs3c+iF56OYnQHYSANHVwH4jXoA8cE7gB7Vmrs+lAhVT9kVKqQUSK5JN04/rbnkf1+g1DaUoQQzZ5TMI6agWyuBN9GO2w8wsgFux57bTuv0hg0PajQx6txP32UnFi7iEestDeqYWJLia5puJ7HI//zd078UX/GnnEyAsErn9Yy8cS+qICCYP7Yc5h452NhJX3+2HNQ+G7SN9L49tDest65Qt6W+D/h7UQi8JV8kEHLenC8Fqmlldb/LfDBbfZSEnFxy0HXTPArsasXts9JPaYg4zuwK+eGHXBCzUY6lejFwwEBXiPSrUUqcRBZGD0n41QsDQThdszDOKQcESkgcvJD+I5L9TN3ANBj9CL0wjJ8qZI3/B4yc5px1txAPLnGD5xMhppHq1YYrmXNdpTso2anepUflcozTyONfQU3Xs+DU89g3JKnyT75vGAPmnclrvM+ZullSLceVBPp1mMUnRm4puy6K0juF0/E3/o8rt2IPuAC4gn3AqGXYPadhbPuQfyKN4M1/UfXIfGD7k7Xo/a1pyg4+VaEpmHF4zy7+GaOPn0ky8qHhfcWK+1Nfl4umm+Gz3Jy/b7vub8x/LRfpLyXnQ0Wri9JN77tW9iuHQbjSWyvrcB29z2NxjRNfvazn/Hqq69y8cUXU1xczJAhQxgyZAimafLCCy+kA/LvIwxdRRFgeT4fVFn8qHsmy64dSWVtAxPvfIzFY35JrGOmWs9Gj55AfMP4DgHGJGyvNliYN0/H6D0N4cexts1rf82BM3E2vYJWMCCRLYzhOy34LQ0INYPal+4n50e/ovK/FlNw8mVoWbmInO44eoxhU+9k6OSFtEiNt+pyqLeCBTlDB2TQqly/9UPuu2pIGHxfuOApmvIPZMzSl2jcXUFzbRWvPbKY/zdm5j6tKO9pzZasWn9THubfBWu2fYmOdiSKdFENk0bH7PTZdmxtN3sNwCztB/5UpNcUWpxJpwKvcTf27nqiBxyCvevxFAuTsP1MZOJu/Qta9+MRZqxTpcbd8jzGoBlIqz4MxiHZuj4Pc/B8pBBETnoAdI34hhmdEgORfgtBNXA+ewy93zBkvAYnIWAU/q01M8MKvXH4FHIixVy99FlefmQpPx91LbXbN5Fd2B1dM/HVDEb9+niuv/dJzj/lJxTmZVGaa6IoAk/RUH2JqghWTLsUVVGoqG1g2bOvMfuSod/G15jGdxhtdhB0G0rHCnnQPpkMyBV3B6jdkCiAD4mAXPF345MOyP8tUKBqdQ1Lp44I9SMyTBNVk8Q33JQ6J+2YQ6TfAkRtDKNkVMrabex3K8JrxNq+cI95UsMoGRW6VSjZZfjNu8HIwW2zKTx9PIoCfvMOnH/OQcZryfzxfJy3UnVi3HVzEf3GEc3PpJlArMj1JE2UkDl4UWh71vQVbM/2pC+1kfG9TlynkYqv+v0n9Xky8nNx/3FtMD6jRejZRcQ3Tuqyyy7pxiL0DIxj7kDJLiS+obyT5ZnRZwx2xZvBmv6PmzAHz0f/4bW4TU3kD/4V9uqpYdLp16Mm4Gjdwu7OWGlvLrp9JQue+jsSwcPTR1Pb2EJNQzP3Pfc3xp9zCkvXplLRSnNNNEXwveUx/ptgaAZlsZKUoLwsVoKhGV9w1teD7/usWbOGPn368OKLL3LCCSegqiqWZbFx40ZOOumkfX7Nr4J0QP4NQFUViEts1eOI7lHW7mqjzfXJz84kM2KwvaqO2Q//jSevnYW7JtFyrmdgbZrUpWK1U/UoetFwFC0P39oWVMkTPqfWlulE+s7Fer2DndOgGShmBK+1gW6nXBS28u5cPhEtvzvFo+9F6gJPy0FVFbIjJvbONgAydMFRpVGcNitQH08E4xC08z44/nRGLHuDaFFvVCNKTmEPhl17FHnF3ampadl3H+I+9EFPo2sk7UgACrOz8aqbOr0m2dquZhdQfM5YrM2TUhNGCf6229xA0z+eJXpAf/TSK5D2Nsxe7YG7tW0ORlk5xkHnYr0+BRGJYRw+JcWaRDtkJGhRFD2rS56jdJqwXrsysDE5fn6XgnLgId06tH5nYL87F+OIqeHfEvkHox80HGHmIMwYRPKx352D8cOJdG94iItuuIWKylYennRRyqItivty1gmDyDANPF9y55n9kS01CN/BFypL//xXXnj7A8qK8rl93Llc+9tfp61T0vhSdKWynvw57iTsAt0dSK045PgmK+R41d/inabREcIQDBoxAJA8ftNYmuMupqkA8a7nJOmgF18YBuPhca8R6bd2micjfecR3zgpdKuQ8TqctTejHzkdLSMCrTuQWkBX0AaMxV1/J8Kp63LOVCNR5B4+5K4naWjrSHf4CsF4B/pS2FX1OcKfaXy/8HW+f0UL9HlUTcVJrsOaiV2xNNGenh1UxaufRC8ajrV5WrDvLSsHI4ZQ60Pxw45IOqqEv7dVghCIjAKMTBV73Z0pySnto9tp228CZ13/e/LK9kczo1yyYCVrPw30OD7ZVsmVw07kwF4lnH/KTyjIy+H8QVl8XNWSwiE3fD8dj+9jdMvK4/5RN3fikHfLyttn10hyyB3H4YADDmDMmDHMnj2befPmYZomrusyePBgzj///H12za+DdED+DUCTguZWh3XNPqaqcHiJSXX1bkbdspwFY8/hsd8NJ8tQaXGz8Q+fS15UgKIg9FjKJOVUPYow+2H0uBqrY7tOB69n6VQgvdY9KoAz0A8rRwWsV8cgosUUD53I7hcfIXfwOdT9eR75J49G07KQLihKHoN7RAhMVCVOmxUIV3yO+nhUuLxXZXFwt2J028K2vX0mvpDEFyqfp0ftt4ZkaztODU7V/JQNpV29CqPntQihACrGWf2RfhtCxveo+iQy3koE6TSE/G3nw3vRDytHySrDb6sCBLJ1CyKrO8bgm3E+fDCgcxAIsgktinnMgkCsTdKlDYof34hdvQSz90yI5AeihtFiiORj/GAU1o4O3SUJfiaKFvLJZeGolCTQAxOGceX9f6MgN4um1jhRQ6OtcgNLy9s7N8pvfITq2mbWfrqVCYv/yKqbxqY3qGl8KZLWZpqqIWUwXpJVgaQPueLtxtd/GJ4jlezweBrfPgxDxdUMVm9rxVQV9s83qa9ro1tm0PLd5ZzkVKEYPVKOKxn9EYqJXfU4esEvEGoeZs+p2FV/BGQwR6l5Qcu7pydoPnmBfeMelmXaIZeEdJ9O4qzxNmT0qy2YmirINNrCCnpLooLelTNH1YqJgYAc/167oDS+eXT8/s1eAyg4+WJUxSLHaKHJzuy6y8LIYdQdTyMULRyfUoJROBRr27yAxlF8IWbpaKRQUTL6B10hZk+kL3HqXsUoPK1ry7OEowqQUE9XcNbObtdPsuvw6z4Egn1xflExjZUVxD2J6+lUNQQFiCMO6MX43xxNUaaP7jTTqyjGnJe3srvFYdJJ/SjOMcjQVXRVwRGQgYKUElfItMDbPoCiKBzcvS/Plv8B27UxNINuWXn7LKZ4+eWXuzw+f/78ffL39yXSykPfBHwwcwxaHYmUknhrC61xiwVjz6FA1vHitDNZfsER3PHbH5Mb1bHenIRExyi5FLfpfaRWCJH+mL1vQAgJSgZK1hEoGf0xeowBYWD0nBws6noJQs9A5B8cXj5p+SQSWXTZVom7fj6Fvx5L7Qu/p/WDv1K5YiI1n65l8cXH0LTrE5yGSpy6SqRtI+wGhFWDqqoMPGFIyluLlfbGQqei2WP1jla0qPmNfIRJ5fM9r62oafGZbxPJ1najuA/SqUAtGkX04EeJHvwwZtlYJJn4diXWjjsR1ILXECaPoGPgPgWh5YOeHY5Vv+5D7LevBUUFoSGox65eSNsnF2FXL8T4waWI/IMDdfSDL8D6+1SsN8YHYnDomH1mdml/Jp0K7MoVGEdOQWTGMI+dgz7g8jAYT96XtWMe+iEXgh9UkGRbJRlZ2Snvv3bnFvBcYjmZ9CyKoTkt3F+e2rmx8vrzGD/kaCBQaHW8dA49jS9H3LHQVQ1FtOsNhBxy2wLpIPx6pJrbfpIwkOjgN3zbt5sGgVDr6h2t4dqOY9M7L0JBpoW1464ubUrdupeRQk2xddKLhuPUvoJZOhqhBpUgHx2j+HxQDCIHPwFGCVLrjlBUnHVLENLp0rJMiXYL6T4dbZ20gZMhu3tCF2Qv358qyBYVeG9ehf3y+XhvXkW2qEBTxec6cyh7VODT+H4i+f2bvQZQPHQcbLkbd3U5zpvl4RjZE4E+Tz98qWEcOQOlZDCKZmJtm4dSMg6j9wwUsycoBqCj97gGJftYQIAAo3AIdvWfu3iuZuNuDqwEk4kpv60a/ZBRiEgM+915aAcMD+9DRItxKzaiv34P3SOSN/71CfdOuZhfHN2f2y/+Gf0ymymOxJG1G8h26+lfnMlVx/Whb55KpnDAs2mIO3yyu5VdrTZrn/gU2eKlxVv3ERRFoSgnRlmshKKc2D4v8H1XkK41fgNQdQXF0Ohf4GP6bVTWNFHT0ExxVPLo5LPDzXx2txIEHiISAyFxGt5GK/gF+DYIG9+pDFVZtaLzEF4d1pYOCqwJWwhr5z3oBw3Hfms6kLB8cuMg272QZVslflt9aJXi1u0iO69b6CM+dPICVs0dz8gFK3nhD7NY98rTxEp7M3LBkwDh7xcueIpPrWxABpuSb+pD/A9UPv+/CgGgqJgHPoKQzak6B31mIbUi9OLzsCsewuwxplMlyCgcirVxSqfKdNKyxBcGSnYh0qkOKRqBoNEcIkctAF9gvT4+ZSMq/DbsihVBR4m5P2CB14peNBy34e/o+Sdhbe5wzT6zMXpORgglhf+uZPXAb2tGyT8EGa+ltTm1bT9W2hshffKzMnj+kzpOLvW67NyIZQWJqaTCuqqKdJU8jS9E3LEwNSOsjgNoiooiFNrceLvfuEhNEqFkIvxG0vj2ISUYqo/2g5YAACAASURBVOCgmEZTfS1nXncfj828DHDxm17H9mpTutxQstGKzke6jZi9fhdWxJVIX4xIX6wdd7ULt/acghXyaKcglSgoJs6Ol0Jrsq4tyyT6QRfS8O5rZBw4HiO/GFQTx9NTVNb3hv+babThvDktZa511kwjc/AiPHvvnDnS+H4iSV8rOPniUCAYUsdIkgqRMtbQQQqqnr2H4nMmId16lMIL0SOFWJ9dhXQqULKPxSy9DKGaGGXjsKtWoWUfirVjSZBor3sp9blSc9B6n4bodxbSjSNVE3f9nch4bWgRqGT3xjhqFu6W51F7DaVy1eKgq+OhSZw9ein/vcXl5ot/SYazHe2jZcitgQWwWzCBMw/uxce7Gzn3lhWh2NsdEy7kyfWNnD+ojN4n9+T1u9dz4oTD0RSB8EGoAkEwRwhVYPleuoKexldCeibdxzB0FelKhOHhNNdzUUL0pawon5W/OzfczPc+7GgumbMChIo2sBwhbfRupyLtCjB6IdzdqW2/fWZiVz7WScTK6DkxWNALzgHas4VSjeCuXxzel4gW4zW3V1W0/O401Qdtj7U7t5DTrTtDJy/Asdr49dU301gTXKepppLTJ93GsKl3YCkRPmrN6CD+Jr4xLWnPk2SXHMBVD7yJ59qomoESycV20hPcvkJHFfvG3S2oalanDZqmCrLNCqwN1xPpt4D4hhtSqRVuLcLMQzqNgYXJHm2betHwTtxJa8c8jIPG4Pzr9+hHTke4O4hv65hoaqdkgA++7MwDV1T0gl8EwXe3QqzNMzo8KzOwKx7aQwRmGkZZOfGNU9uvUb0K2VqJ84/b0A8rByOPrFaDy37/HEY0E7uthfzuvWms3oUvM/hZD4GmqNzwlw340sdzHRoqt/O3R5dQ22yFCq03LF3F+HN+QWne11PqV1UFTQrwAYV0a9z3FHHHDgLyPY4bmo7lWGFAnmxTT0KKjHRA/i1D+hJTURFCUhiRbKms48GnXmDGyCHkm0HSUuglCQHWaQBBkFF2AEKCL22kko1R8tvUuaqjcOu2OZh9b8XeNjfU3MADfb9TsT57HNm2G6V4MFrvUwNXFacJd8vzSClwml2Mon7suOtKtPzudB99D02eQZIfvrf8X0W6uF0E/Yp0aSZ7r5w50vh+IklfUxWryzGi4ZAbhTYvA6N5e8o4Kb5gHmpGLkgP6VZj5B5B/LNAqC2ZtE8Reet9HajdAh2F6lXoBb8InyuhlxDptwAUDRHphmzahLv+zrA9XejZQVGqtSoQbj1yOjUvP55SjKprbmPG81t54/J+qOtu78Q3N46+jfELVoQuCtur6rj69ge58aoR3PDcx9x7zkBaauNIV/LKon/SUhsnMxbh6N8ezMevbuPgE3sRydEh7VuexldAOiDfx1B9gd3m0Oy5oQIrBA/0pqp6YqW9ye5WwkXX3U7dQxOIjLkXLTMHP74JxegBSgQhFOJbb94joJiO0WMMVtPr4bWkU4EQWtC2HikicvJDIGWQsXZbAxs0CIXeqv58DxAE41m/uZYHbpoABFXAaG4+D0w+N6xGn3/TA+hGlPsnnU12txJOvfwGCnsfyMCIhRdRsHxBbkExbpv1zXyOqoLb5PH63ZvDye7YywagZgamE7ai4PoSTREYfnrC+6r4MhV7TRVkRtvQFBspszD7zgX8IBjvoBasZB+L2eMKMPoQdN76mP2CRBIigqJldynIouT2RT+sHCGbsbbN3yPRNK9d0MhqQCiZnXngFcHCHek7l/jGyXs8KzO6flaUSMo1In3nYq2+Nai4Z3SnqaGN1uZ6nrjxyvAzGXH7n8gt6kFbUwV/uWsmP7/kd9hWC49cPyJ8zciFK7GzSpkxcghzH/oLaz/ZyvubdvLUTWOJ6ga2s/ct7KqqIFs8Xrl7fadxn17Yv1+IO1bAGZepwYyh6sQdG5HgiUslK+X/ZbpC/q1CVRXqdzbz2h/WcfT4w5m06gPm/aovY884Gdd1MbQ41o67MXtdGzpLKNnHYpRcGAYd5n63gt8aJtkhda6zNk8L5ihU9NKxODvvDOcroeqBdoaiox98Ifbq6SkCrlIx0XMlWnYWGYceT94xw/FVI0UNem/5377QuuaiC62TM4cvtLTK+n8Qkt9/jtHS9Rhp2oi3bglZR82i6qV7UsZa5YpJ9LjsTvymbbh+NUak3xcn7bfcFCSnEhVyqQQCbslut8BFyASh4H76aHswnugODYpSZtC+/s5MsgdeRtOaZ4Bg/1uQaXLRUaWYKqHYXBKyrRJFSIryssP9OwR7+BxTYWeDhe9LMmMRmqpaaakNBDhbauO8/dBHHHHmAbz14Iccee6BZHfPSIvApbHX+M9s1P8GIT2JG5U4rpvyMAPMeeINRi58imFXzaL5mVuI7j8IRYuD9HAqHwShIf044HWtKqmmqg4KvQS0Aoz95uDHW7HeuAbZVo0QCs3r3sb48XzcH93Brtzf8vjCubT0/Rmx0csoHLmEVXfPY8t7bxMr7c3Ftz3Bn2+fksKLffi6i2hp2E12txJ+Pe4mnrjxSm781QEsGXEcDVs/5r/njqFpy/tfGCSYERUz28DMNjGzDTIydKKqRgSVqKph6J/v6KhJweuJoASCye71u9ejKwpVts+IR97jV3e/w4hH3qPK9vFlelPwlfA5KvZYDWFV3Nt1B769C4GNtHcCaqgWLPQYZt/bMUouwNpxFzg7iG8ox9p6I9i7cKr+hPBbEq4AJSmXFnoJSBnwx1X9c8e6WfY7pCewd7yGMWgG+iEXduKBS7dhr58V6TWlvEbazWHbvJQKvhZlWfnQlM/kvglngoBl5UM5akjwTCSD8eRrlpcPY+uWzVxy6/2s/SRQbd1eVUdjXRtuo9tpnKuqgqmomKiYiprCQ9tz3EdyDOINNronOr02je82gpZ1vcsKedy1EH6iQi72cJUQGQivsyNCGt8MNCl47Q/riOQYCE0w6aR+FGQZtFk2dnMtrfWV4NUihY5RVk6k30KM0kuxNt8Qzk1CiSDUnM+Zq4IOCKGXIIUKaOjdL0f68WA/ICXSa0SJZiGdWojkB+cmBFxp3oT1ysU4q6dQ9OtLEd16oqg++ZFmcqPxr8T/brGj6INmp3DR9UGzabGDoN3zJM1elEY/m2Yvmg7G/8PgeZImO7PTGDEOn4T76aPBmFx9A3lHnwYEVqmlI+fT/YJZCDMDES1Ey/tZcF6SE652nbTHaw2TVoqWFTxXZeVIoeN++hDW61dhvTEBbcA4zJ/Mwzx2Eebg2yD3AEQkhvCtQPCwrRI1Jwa0F6MennUlZ5Q2UVHfFr6PJES0mM3VTZSfc0rK8bKifBotn9JcE7fR5qeXDWT9XzanvKalNo6RqdFSG0cz1aDLLY009hLpCvk+hlAFluuxpaKGsqL8lKC8qqEJtVsv8mP5VNbtouDUi5FePahFSKcWu/ZFtLxjISEAs6eqpNByw+PJNnZn93Po+ScgPYk+cEzQxuPHyRxwFG2uxrT/d1j4N95auRyA6c9vYtjUOzh90m34roMdb2PdK0+nvI/anVswopmcPHIKj05PVZ5+dPoohk5ewLKrh3yuDZkZUXFUjXe2W7Q6MrRTe+/xD9jx3u6g8jd6AEaO1nUF0ScMSpJoqY3TCoxf+QE7G4LK/M4Gi/ErP+C+3/4AV1XDinm6mvjF+CIV+5xIG27ti2F7pdBj6KVX4YsIilkWVsmltLE2zw0qPIkMd/DznPCY0GOYPSel+O+avWfi21YQCHse5n63BloJCW63dGoReiF+UzXu1j+j9zwF5+MVGD+4FLkzeCaUjP7oRcMRWt7ePSsJn9OU1+jZGEffAkYurg/1Fdu6/Ew8x6F25xbyS3phZmZz/k0P4Dk2iqohFIXWhloyi/NTzisrykc48PqD6zlpwuHh8S+tgHcY9wV9cvjBb/ry9kMfddklksZ3G0kOOYDfLPFbJWo3ga7qxBMt61JEQRgg2+dIqWSAkw7IvzX4QWJs0PkHUdXmMO+lDdw1bH8mLP4jD00Yhus2oxdfiL1lVjgPmfv/IYXzKqWL0Aq6Vov2mto1YSTg1aKYffDdelBMfMCuWRJ2BXXU4EgKuEKCy/vJQ+gHX4T95g24iSp69qDZuHr+XvG/XU/SpH59n/I0vv/oOEY0HPymjTgfLktRNNdyYqH4m7t+Pl4kH/WHl2Pvfhyjx5VYO37fvi9IjP89nwvUDCL9FiaeH4hvKA//L+lBLiIxhBfH/uf8lK4Re9PTyN3vYhw1E6V4MGp2EbHRy6jZtZUHbprAlvfeZsdH7/LrOc+QNeA6IutvCs+PD7iOaxa+wIKrzwv38EkO+aPr67h9WH+KozptdRaRHJ2fjh6Ikalht7hsfGsndotLZiyCa3lByfNrbkV1Q2FHXSWOcNEUjSw1C8dO72u/z0iXW/YxhCrwfcnCx/6buyddSFlRsFFP8kunLX0KSypo+d0RuorQCpG+j9lzEn7dc7jVT+BLFbPnlE4K0nbVHzF6jCHSbyGRfguwK1ag5x2DtWU6SjQLZ92SIGv45mQUpxHDEF0qldvxVnzPR88qxvMlumEy8IQhjFq4knH3vcqohSsZeMIQ7LYWMnJjXQYpyeO+5xDJitDq+ESyIu0VPF3lnZ1BMA7Q6khW72yj34k9gUTF+571aELB0zUsVcXTtfbzFciMRVKumxmL4ErCYDyJnQ0WuxqtlIp5upL4xfg8FXvdjKIoCkbhEPAdjJ6T0UuvDkTUPrsc6VntVXIlElZ4wkpQ4ufkv37rB9gVy9rHbd952JUrELqPcfwiRGZ3AOxd92DvWIJRcilGn1vw420o0W4YBw7HfncOfuWb+I1bEXpJwDkrGYW9YwnWtlu+/FnZfyEYZejdLw+dCcweU7DW3ILz3sIgk27oZBcU87unP+DooSPpfdjRjLvrGa5/YjXZmVHOmHoHkexcmuuqaW2o5W+PLgEBzyyYwqq54zHcVn5xdH8geNb/cPUF5GVkEMkxkB02s1/U+eHpGk2aYNC4HxDrk8NhQ/rSJCUDRg/g+Fk/5uiJh9MS1XBUNd0R8j1AsmVdepLGVTbNzzq4Hydb1i2EVwtqXoroG4AUmQjZhiCtbv2tQIEBv+xDXFfCZLDnS7ZX1VFcUkx2bhmKURbOgUpGf4QAe8cS4hvKsXcsATUfhNGFWvQM0AoDik7FvSh4iVZ1H9RcEDru7mc7u0McFChIi2gxskNyRut9KvbqGzoJbimqT9EF89Hyg/k2lf+diqRPeV08i4a2SDoYT6MTkmPERcdZtyQMxiEYk0pmAd1OHR2Kv+kHDcfafjN64TAEMhBATOwL0Aox+8zopKLuNKxtf378JpSMYH1NepAr+YdgHD4ZFA194JhAmDXRNWIcODxoV189HWPAFcRdjRmn/5DFV/yGLe+9DQT7WA2Pm1euxzliDtrPHkT7yQJuXrmeqvomTMPgzzMvZu3isTw3awQH9CjimpP6kRvVcTSo3lTPQWcdiJVnUBd32fDWTgb8cj+qN9bx4wsPIatbBFd0fnZ0Q8FSW2kRjVhqK7rRea+qGwpbG7czdPE4Bt84nGF3jmNr4/YuX5vG9wfpCvk+hlBAUxWmjfgNeVmZzBg5hLysDOqbW0N+6cxRQ8g55xakMMCrQ7q1OLuf7SCUtRv0kiCQkB6+U4296x781g/w6v4STFh9bw3E3HpckWjdbe5kh2Ies4CRC55k+fgzUjixbz91Hz89rxzb8cmI9QSvlV9cPi3ldSMXPElGTozKTR8SK+2dEpTHSnvT2lAb2JAZJm/uiKdUwdW4BYgwGE+i1ZEYeUb4u5ljsL3NoTyxySnNNVkwrD9FhoKL5NjLBoTBS7I66AgozTVTgvLSXJO6lkBRPqyYn3cYarpK/vnoQsV+3PJXyMwWCL+GeKLd0tzvVvBsrO0LEXoMKS0UozQYc8nMdocMd1fHkkJHQi/B6DEGv+l1rPhnGL2mIqQXjP2SUdgVy4LqevFE7NcnB9XrSAH6D65EmDlIz8HoNRP8urDiLp0K7Ip7McrKUcxe+NbWUBCu47NibZyK2XMKetkkhIhir54Vepzba2/FPGYBumyisaGGUy69FsNqpO5P02ms20VrfncGnT+Xh+dPYt3LTxEr7c3wmcv464o7OHnkFJaVD2N5+VBmLn2N6y8+HQPBuhUb2NK4gyPPPRChinY+ZxedH2aOwc64m/IczDvnIOJRnav+tD48NutXB3HHq5uoabFZeMahFOppwZjvMuK2jaHpeNUSEoYY3k4ZiLq5NsJvSgi67fEdK4kgym8CJbUzI419D1dIsosyaNJEuO4IReWK049Hb6mm7qV7KTp7Qjjf6UXDQ+E2AKHHEGoEgYsvdIyeE1GM7vjWNuwdixLilYlqOT7SjyOQCGdXYBlZeglezR/D+0kGJMlqoLNuUfh/wsjrUoldkS7Naf73fwz2RlF/X6DFjpI9aDbOmmlhhVkfNJsWmUdWscBanxBjjRYknoNMkLJrAcS+cxBCx3eqQMtDyz0KrF+i5f4EfAuj52TsbXMD2obnoB8yCuvvU9sr44dPCiv10mpAO2A49uobaK6vpbHZZeAJQ1I6QWOlvXFRmfyL/tQtHRMK0E0562auOvNk1KZdVO/YGAq8Rov6MO3vcWpaHW4b2p/YEUWMePi9cH2ec+qBrPvLJo48+0CE0rXKejLQvuS+aWyvraAsVsLSEbPplVOWUv1u9prD1wBsr63gkvumsXLsYsyvYGOYBpx44okYhoFhGPi+zxVXXMHPf/5zbr75ZtasWYOiBL7yl112Gaeddtq/9V7TAfk+hKoKNu2uZdQty9leVceD0y5hxvKnU9rWy4ry2bSzBk01OUr6WJsTqtXdL8PaekuiHfhSrC3jUbKOQC86B0UvxOh5bWDV1LwWs88snKrHExlFJWi/VTNQ8g9JaRvyHIuN777BuOWv4jo2vufy9lP38aNfnR/ah9mOj+raYTAOCV7s+DO4asXfKTjwSEYtXBVya5PByGuPLGbUoqf5OJ5Na0L5PFkFH9wjAnj8KLsJxbNxFYNNTg62D3aTHX4W/U7bj6u6aj8/7zBUx0XNVDlh/A9T1KYN32fBsP5hpaI012TGLw9k8V83h393Z4OF60vSjb2fj6SK/bj73qC1vgXDzCA3vw1pb0wRHuoohGb0GIO9ZRZGz4kIvQSn6tFQrTzZfhYcm4Jd/WTnVvWEenry7ylaPvGNU8L29qS4kTByMI6+BZHdG0QT1s4lqQJyZKXYo/mtH+BUPojZ63oUoxS9+EKcygcD+6A+M3GqHm93JSgrB7UkDMYheFakXY/xz2sojBaj9bqB3a88nCqA9PBkfnbWaNa9/FQKbaNov4MZd9+rtDbUEnc8xv35M5addxj7n96Pz57aSHZRBp4iUVGwFYUmCYPG/YANf95E7eZAmKur52DScx/zu1P2Tzl2w3MfM+mkfoxf+QHlT76fTjp9x9HmWORGsnErgu9Q765iV7gYxYkKuW91SQeSIhMARTbikQ7Iv2l4no+p6yhKezK4TaqUn3YUlfdeSuGZ1yDdGozeM8CrRzF6hOrReslIFL0oCL6Tc1LPKdi1L6PnHhUEFySrgjORUkWYQReZFGqiopi6kgm9BGHGAoqakZMi3irM2BeKsnUUcEsro38/IX1/rxT19wU+j+IgPQmKCMeiNPIxek4BtxGpRDH2m4O9qd2W1Cg8AyklUkoUvRB8C4SGVngW9qZrU61+tRiypRHn3Zv3KELNQx84BmfdEqRdH6qt796xlRU3jGHs8leBdgvfixesAjNK6xMTUtb6lid+R8Eld7OrZleKwOt5N97HNT/Zj4tXbuWaVR+w9LzDUtbnKc9/wr3nHkaLAvrnWJ41e80seOEBZp4+lryMHOpbG1nwwgPMHnZ1SqDt+m4YjCexvbYCz09LxH0dLFq0iAMPPJAPPviAc889l127dlFfX88zzzyDqqq0tLRQXV39777NvQvIJ02ahBBfbHA1d+7cfXJD32VYvhsG4wALH/tvbh93LhMW/5HtVXX84uhDue7C05BIBAKBk5hoYkgRCap8Rg/iGyehZB2BVngW0t4FSS/ywrOh+BKc2ueCwLznFHwpMcuuxV53T5gRhGARrtq2mZW3Xs2nb7/MsGsX4fs+Pz2vHMxUO6bP5RM7Nv9si2GY+3PR0jcwpIOuKeiaxhnX34WRU8iaTa0p57U6EiGgfvPH3Hf1kHAyu3DBU8T6HMo/HwwSBpmxCNGiaJft58lg2vP8doXKDnNbkaFw33mHBa9TFeb8z2es29netleaa6IpHaqSaXQJQxGouTp5BTmAh+8pKGYZZq+p7Xxu6YJ0E0mfoA3drXsZs89MrM3TsSuWoRdfCEYPIv3mI91GpHQxelyJ9J1El4eLb23vYGVGmEzq2N4u1OwEtzsT+73p6D+4MuBOdrRG2XBNSoDv1L2Eln9iYtO7NSUQR83Hqbwfr+4vQLvSulD1wJ80oc4qosXIeELRuq0S9x+zyDv6Mlrf/2v4Wbl1u8jO6xb+XrtzC1mxImp3bOLuK3+VUFtfxfxf7YcVj3Pnezu5dPgBqBGFRitO3PXYWm9x79sV1LQ6zD3rIHjiM6xG+3Ofg6ihdjqWE9XCn9NJp+824o5FYVYMt8JHj6moRQJnl6RIdmOrtwOkBaJ75xMTFXLhNZIeAN8OotkGjS12mAxujHsI3QsCnqxcEAZCWljbF2L0GBOorBcOTUlIGr1vAL8VhIZR8HN8aYZdcAgV39cR/m6sDdM76MTMQiqRVD2M3jPxm3fjbnke/dDL0Qfdgu/E8eoq8KSJNnAy7rq5YeVQGzgZT2rkRuNh0NTSgRceVlOlg1BUJAJfinQF/TsKr6VurxT19xWS7esQ2KRmGm2oOIBEP3I67q5XEX5zu5NAYlwb/X6PIuzE+NeQvh50522YlhKACz0WdsNZ2+YQ2f8OiOhddoIIIy+olG9YidbnNPz+E3l66hhqd26hoWoHv7hiOqdfuxghFBa908jUH2ewswuxQx2vk3jrI9eP4LKlrwDB+utLWDCsP8vf2sa6nU3sbLCoaLaY9uzH3D7sEAqiLppn4HkS3VBo9prxfJfrf3M5Nz7zB15Y/zplsRJuO3cKyh7xlaZolMVKUoLyslgJkuBvfd+45NKXxJttfNdH0RQiWQZC2femyv379yczM5PVq1fTq1cvVDVYQDMzM8nMzNzn1/uq2KuAvHfv3l/+ojRwPT+lGr72k63cuuI5Hpl5BVFDw3IcquuaqGlo5olX1nDPhF8h9BL0kktAtqEY3UEogYhW8cVg70j1Iu85BcxM9OyByMwDkFo+2K3Y/7w7UIve/1wgCMa1H03n8asvBoKM4G8mLySjWw/amuKwxyJrmBlc8+D/kJGVTWPtbp5ePJvG6goUVefo0ihv72zj3eZsMnTBEQVR/lllYXk+x+QJMvTU1vQMXeA07mZZIhiHYDJ7cPzpXL3iTX509gEcccb+gfidBvecVkqm6lFnC+5+t5maVudzg2lVDaqMli/RgAwkeB6XH9ubj6taUtreDd9Px+NfANNQ0PUqhFuHteNJ9MLTE0Htdtz619DyjsPodQOgIt2aIDPtxxF6CVruT7ArVmD2uw0h3UB4yPeRXjO4NaEwW2T/O/DtSpyqxzEKh3aqBvlOdWp7ux/H7D0De8Of0X9wJUpuH2RFwhqlZGQnaxS7elXXvr4Vy7A2T8fsd3sYjCevG7SDKiB0tAFjcT95GL3fMJwPl4WvS4rSdISW352m+t3h77HS3mQXFKMbEUYtXMmLy+ewvHwox037I9c++CK3lV/AC59Uc8p+0dD+sKwon9vKL+Cu1TVM/q+PuX/0YUQ9SasquqRhtNmpI7g016SxzQ1/TiedvtuIOxaGquPtlmTup+HlBF9mkVvIJ95GhN+Er3RuT5Qi2bKetj77tqBoAXczP0Nn9q8PoldeBOG0BUJpzQ2YeTGsjcE8FHQJTU2xYxR6oPqctHhMBiV27cvgN6IXnY0iJPHN0/ewcLwBc/9FmH1uRCgmUmg4m19EbvkLxlGzkE4j7iePoPYaSv3bf6bgV/2oWnkHBSdfhpaVi+84eNJE93Zjr04VemtSS5DQqZpaeOY0Gt54jLyTR38jVdU0vllI194rRf19DU0VZIsKnDenheNMHzQDff/fYG2YkLp2VzwY2AImqHFJ3rhd8cAetoBzMHpOBL+tg0CiQChql50gwsjBfv8e9IMvpGZ3Mw/PuJLN/wrchJprq1g1dzxj738DN1rA6q3b8X7yOWKHKF0XqRIV6tJcE9vzmffShrBDs6bFprHNZWeDxYSVHzL5xEwKs0x65pSypSG1Tf22c6dQ3VTL2i0fcM0f57By7KKUa2WpWSwbeSOjll+fcs7Mp5Z0qqZ/1yF9GdpKJumpx10+kLzSrH0elL/11ltYlsXo0aMpLy/n7bff5vDDD+e4447j5JNP3qfX+jrYq4B87Nix3/R9fC+g61pnZfX6Jjw07D2C8bFnnIyUKmbfBdAhe2j2W4TeYxJC+MS3zek0OUX2X4hETfBl49ivjAYSk1GkG+Zxv0fombS1uZw0YgoZuTHsthaq4gp6g9WJd6qpgkylCaN6GXJrJZnRYi65eTGtThZWk4Vbs5Vj+5QhdZVm2+f9Kou6eDAprauMc3SPKG/vaEvhkNPYyNDJC8jIjdHaUMuLy+ew5b23cW0bRwSLgoqgbecGnr+mvYp+3dwniZQe2mUwraqB3dn4les78c2LDIUVFx5O3HbTKut7icxIM9KqwN79LEbx+VhbZgaJoO6Xo3c7PVgwC4eCMHB23YVecgnC6I3ZZ1bQTubVgtdEfM9guOaFkA+OdEEYGIVnYFc/mdBIyEPoMXwvjlO9sr3lvc9MZFsLUmSilw3C2jEPIzImUTGPoehFnaxRjKJzUriae/r6CnyU7GMDrYVkRJU4QQAAIABJREFUu5vIwG9rxHlvIcagGWg/nAjNm9H7j0Y6TYF1S7wWNbMgXKi1/O4UnT+Xh+f/DgiC8VELV/LmE3fz8v3zQxrHs4uvI5Zlsr2qjmsWrmDF9Mu5YOYfwvkgefzGq0ZwzZ834PgSxfcwhNKJhrFgWH9MTQkD9Y4c8tJck9uH9SciZJJ6nMZ3EHHHIkNGkTYomQI/UbzKdXPwpYWQ8UBlfQ8kjwmZVlr/NpGhSBpd8H0JAmxUCs+7lYZXl1N8TnvA4bd+EFiWdRB5M3pOxeoQoCeD7UjfefheU6Bx0Wtq17Zo0sEXGk68Ek1x0Xv+FOuj+7BX34B+WHmQUNywksLfXI3v+HhNNexcPjFUuRZKE/bqhZ2E3jIHL8KzZadqavWfZlPw6wnfaFU1jW8OQjP2SlF/XyPTaMN5c1rKOBO0IWQg/qrm/xK96OyAgiE04hvG7/E8TAvW7qbXw78pnQoUvShMboWB+851GEdMxV57azuH/KgZSMWgpfgMWrbVc9/Es8O95aiFq1j/2nMJIWKbeFsLS88+iFZFITLsRuIrrw/X+siwG4lLrUvtpF0tQfL8tqH9efDtbexssJjxl0/43Sn7Y2hKSJ3c2WDRr+hQLKeGuGzrxAe/5o9zmHn62DDg9vzU/apj+xRk5qa0ts957l7WbvmA6aePwYSw6u76320V9nizHQbjEGjsvPaHdZwy+UdEc8x9co2rrroK0zTJyspi8eLFHHnkkbz00kusWbOGtWvXMnv2bF577TVmzZq1T673dbFXT+i2bdu+9DU9e/b8X9/Mdx3Z0ShLrx3JJYm29bKifO6eOgqrrYkLbr0/PDZ/7DloCgi/DunWYHfg7KJkJHyfm7v2ZpQ+6DF8TNwPfw8kfSCn4LdVI6SD/fFDmAPGsGru+HBCOmfek/zXFovLjshDURykotEqM7qcRLWPbkf2HMctZx8TtJvf+iRFhxzG37e1pdxPRbPHwEKfwT0iqJqK53r4lkVTXVXKtZOcc0XV2wNtq6FTFf3xyWcw7oE38cju9NnaihIG49CZb16YZVBdbYGXLhruDQQuKBGMkhFYm69rtzLzW7G23pzC65ZOLdbGCUByczklUFrvKhjuOTH4t6wcUEEqSKFill4GSKTQ8WVQbTJKL0F6DmaPMfhWI7gtKFmlxDfM61BpmoSUDr69M8UaRc3/JULP/1xfX6GX4FvbMXuMBa5ItIQquP9ahNbr1FCN1Rw8H/uzP+FXvtn+HOm5PLv0Dg49djS53UrIyC/mpU+qGDJ1ET+5ZAY5WZk8v2BiKBCT5JRfNO8xNjUG43N7VR2qICU5lzyeYyopFW7P81NoGMmkEhAck1DRZOH7kvIT9qOxzeXu17cw9eR+6Y7l7zDijk22E8x1SlQgVIGIQLabSURPaG0oXQRDiWNpL/JvF3bcJRLRKcg0qGl2KMbiiduu52dnjQS0cH5SMvp3oN/E0EvHIqDLuUq69eC3IPQYfI6Fo5QuOBUYGfvhew4iodyctDyz196KPnAMQhE0rF5Ft/PmsvuRyRScfDHu+vkYR0ztsr1XxUEKNQzczF4DKDj5YtSsXJTMAtTsgrCq+m2JhKXxv4eamU/RBfM7c8jJ4JvSDVBVgSIkysAxCD07TG6LSAwQqAXnosdOwNoYJJ0i/RZ/ztqdl3JM6CWJtT/WQfS4Br3HT3A+XBGMeyMvqIy/OwcZr6Uh97c8tWh2SlEomp3HD04ayvb338GRCsdcfjNlRfksu3YE2Tll1J56I7lRjdo2l+xoCZrIZPj8lTw6cVi70PEdT6OWdGfpeaU8t76Cp96rAoK9aFl+lGnPfhxSJ0tzTT7c+QnTVt7KspE3UpQdS2k/315bQV5GoA9y6oCfkqfkIDwl1EryPB9fwvSn7uzUtq4pGrq6d+Jw3wX4rt+lxbHv7ruxmuSQd4Rpmhx77LEce+yxHH/88YwYMeK7EZD//Oc/RwgR2q90/Dn5+4cffvh5p//HwHddencvZOUtV+G4Lj6CiKawccOn3H3lr6httljw9NtMvPMxXllwKdbmyQFHrMPEpGhZxD8rDyafrhZnVOytMzF7XY8xYCwcejkoGvY7s9H7jw6FLVrrd6cEu68vu5FLJt5C1d0jw0m6+ILbUIxs3C4Wa01Tw3MfnBoIvGXoWZ3a030kVrNFYWE21fUtqG5jKACXPP/R6aMYs/SlUEgOvoC37tpd8iJdX34h3zyNrwihIv04il7YwT98XlilSfK6napHMXtdi7X1lgSPqxbptaGYZV0uqIpeHFS0zTJ8X8dZuzCw6MnJRcbrkJqBvW1Wh2z3TKwdS8IqdiRvfkqlya5YFoggVT0W8tYDJeNz8K3tXT8jfjxsXTfLpmL/6x6MAZejINH7treny7ZKpFUXWAVVvhm6EyiD5rBx7Wu8fN88AAaeMITTp9yB9FyKuxUgpUxRa4Vg7Gbmxti/MJelUy/miVfWoGtqp46ZsqJ82lzZiVbheT6q5wdjuUNSSfV8XFVl1MPvdfoKJ57YNz32v8OwXJtMJ8EHTzg8igxBhpVBNBIE5JJIF2caSBRIV8i/dbg+tNoejhXHVS3WvfwUmXkx+hxxTNCCnugssnYsCZOJeI34XuPneJDXo5h9MHtOxXfrU+baZFu7U/UYeuwUkC6KqiHdYPOatDxLcmfdhmrcfscx45l/cd6pN9KztBvxjyqRTlOX7b1O5WaIFJDR/3i85prQMzrZblw8bDKeZqJ6Yq9EwvYM2qX/7+dk/idCKApt+1BR/8uSMaoqyHCrEVjY65akqJ6j6PhONUbR6cQ/K29P4Hv1XT4PQstN1UvoMwtn91MYJaNSBWL7zETr+XPsN68BwDxmQShoXHbUAH4zfg6tDbU8s2AKTbsrOOv636NHopx53RKq2wQrJp5FLMukvnoXWX36UaN1w1EUGjWfB96uZsrPc6nL3Y9fL3yJfEOSnZlBDVnormTxqxt55dPa8L5Lc00EUNNih7/PP/0glr96D9trKxi1/HpuOXMCF9wzOTynLFZCfWsjpw74KXNOmcSrt7+X4iakZqpkqVksHTG7U9CdpWZ9r1TYFU0hMxZJCcozYxEUbd9zyJN455132G+//SgoKADg/fffp6ys7Bu73t5irwLyjz76KOX3QYMGsWbNmm/khr7LELrOG9vbMFWV/fN1lr+xhUv6xnlx2plhlu2mGx/hwbe2oAoXL2EDkTIxSS+lOpgyCfW+DqSPXnwhUuiBi3zrboSZh3bghUjph4tz084arl76LDmxbjTW7kY3c6h5eFJKa1rlimvoMXZpl4t1Y207XzYQeLM4srQg9BbP0AVHlprgpPKSPi/QRqh7TOJGly1BKVX0DtCUrnm2aR7t14OPAXoxCDVFsG1P2zK/9QOk9DDKygPfca8JZ+cijNKxXW8whYLe/XKkNFCkA/E67LemYx6zAOuNCYj8g4kMmoe0asHIxa5Yip9oUZNORacg22/9AN/ehZZ3HHbFijBLLoSGXflg52ekzyyk14xdsRTp1OI370TrfWooHJO0RYHEhtauR8nej8hJD+C37cb9cCma38ilty7h3qljkBKOO28cS0Ye354pX7iqSwuV6q2fkl12IDOWP829Uy4mNzPKsqkjGNWBQ7506giKCnJx/BZapSTL+PI2s/TY//7B8Vw83yPTSQQtCetpJQrRpihZmgsooHQRkAsBIgPhpwPybxsCyMvQieotmI5gxlP/JDOvG15jJVp2PmaPscQ3BIGH7QWK6tKtw951Txdz1cxAi6P0MqxttyKdWozeNwQuFsJE6N3wpYrfvBaZd1ygfSE9nI8fT7F5Sqqrb97dxKTlz1NV38RFv/wpUgQ8W/fTRzEOn4T97rx2obcBE6lctRivqYbul96FX7819IyGhLjlurloxywm6rV+qUiYqnYO2ksuug3VLP1eVNKTYmVdieL9X8TeKup/2fvq6nvdMxkTpRWadmFvWNxJ9dw4fhEII9zXJtHl/rbnJOyqPybEjbvj27uQSjZa3nGd9GOszdMx97sNaE9MJX/2XI/FI34WKqNn5RfxyoO38+nbLzHu/r/SIwrdukep2bWVNU8sp9fl07n/HZ91u5oZ2D2Li48sxm5roaYxzv3vNLBuVzOnH1bEpcfk02y5XDK4VyfNIkGcZecdhtXsYNVZVDyzhStPOI9PKjbxj63vs19hWSjSVhYrYdnIGynIzGNw2RFhMA5BZfj1u9dzwvgfYtke/bv3Y+XYxZ3a0l3RtQq767vsmybvbw+RLIPjLh/YiUMeyTK+/OSvie3bt3PjjTfiOA6KolBQUMC8efO+sevtLb4WqeTLFNf/cxEInLU6Hq2OwnkHR1h+5akp1eKV15/HNfe/jvSCwNracVfKxJQMkJLVwZB3axRhV61Cyz4Ue8eSIPCI+zj/uKk9I3nEVJTiwQgzRkGxhvznH5AVAS9cP3IGFdkFKbwit24XviswjpqNvbrdR9I9eAJP/25c+LpYaW+EaqB5Lj8pMwi2JRIcFyueGhHsdaDdhQ/2qDueSqmid0RXdmdp8bavD4GPlCBRUwTburIyE0IhvnFqyvlBFXoKVkLnIORoOzUoRhE76xRKsg2MQTOw18xor9LUfYRfvwVn3RKMo6aGwXgSTuWDmH1mpyiyouahaFmBf3ni9ZGDAjX19mckO+BtKtlYn44O7qfHJOx/LcM49EqkVYeIlqRYBIVqrL1PDe7n8CloA8YhvTbUD+Zz9pQ51FTV8uj0UamWgOVDGbP0JXZ8/M8UWsazi6/jvJtWsL2qjkvn3M8TN42lRyyPlTeNwfV8dE2lyann13eM+EptZl2N/TvO7FprIY3vBiw3qKZE4hGUqMBXJPggogLd0YmZAa0jFHDbA1JEwW/+1u43jQASyNAgo6WBqoeCBHfkiqUYsQKcTU9hHHBaajLR2gpC73qu8q2gmr7z7oR3+bTAVrLHGOKbJxLptxBFiwXzqhLFdxtQ9HyM/Yfjdz8e58NlyHgtxlGzsJRcsmNR7pzwWzRVIaLqOJ4VKq47Hy5DP6wcJbMU35NUPHwD1tb1ifekYBT3Ib6+i7Z2aeEL7UtFwqJ0DtorHrjme8FB70qsLCmK9385KP8y7M376up73TMZo/gOIhLtstMy7KRN7GtTut+qVxHptwASlDS7YhnSqUV0G4q1816MkguQ0vvcbjwhJErxYPSDL8LZuCpMNDXVB8F5Uhn9/Bsf4MhfnocQBMmlByYHSur53Rl62bWs+sNMbpm4CMs8EM2PU13XxIeba3nilTVcMeQk/ntTBr88tJj5L25g+JE9ePSdHUw6qR/5mTr/n73zDHCqytv479yWNpne6SAoTUQsiO6q6KqrqIBix4IKKqKgFF2lq0tHsfeuu6sv2NjVtTdcUVGEFUWRPjA105Pcdt4PmclMmKCooLLM8wWSySTnTs495zz/8jzZAYNMTWCakvfmfZ6Q5a3aUsekYZdz3Yuz8GoGi69ehO06aIpKqh4kGnERKEnLtRtdhTRNw+P4YyTbAatBG2lnKuyaou11QXqhCNILUzhhYj9cW6JoYreqrL/11lstnhs8eDCDBw/eLe+/O9HqQ74bIaSMq45XmS4dPSTNFqfpgoolC8k7exxuzQeYTkV8o3ZdES/Ndeu/aiDf03FdCz37dMzNM+OCMEbO2MSI5IpZeAbMx1UMNLcK25sZWxTDxVifTiP7pDFsfeCa+Fi0jAIqNtay9qM6+p+3CEWxkehUl5ZRXRq70WO9My+getNi5DvyI3f7LhJtx5H4c/djzGMf4joWiqq3sGNrjp312baKt/08uK5ACJCOCVoGONVJrcyMjrfHbPp2zIYLHXPbvU0HTKcGc/uDGAUjcRybMyc/xNJZY0gzUvEcuRCJxDhsOubyqQ0Zm0lIK9zifaVVAWp6i4y8XjAyoUdTulZ8vNENk5t8fFHxdrgDGa3GXPkwREIITzquY2FVrMEzYB4yGkKalVjrFscV1hvL1fUDxyIUDRkuJqdNe6K2lvQelsCwm+/B8AWor6rglTtvoqZsO5aMbSJbSkI4joPr86JbGroKUaWeCx6c+JPLzJLN/dw0L+VlrYRsb0XUilU7eKMe9FQ1vo4Jf8wQs9DjB+qTiroBoHgRrYT8V4frSvxuTZyMAzi1IcjKRsvpiZRuwppmV32EljMsHrxsWqsaK3liVpAi90wgUQNDuhGEMJBCJVYtYeBKL/aGJWjZvdB7XonwZFC98n28+6lkGSkgGsuKdeodD341HdFlDIrXh10XBquaspfmxsl4o9iXxE3uX+64COSPioQprvWbKHvvKTTPHAtFwVr1WFJRvEa7r70RASOMvfqxWB92Y9/3N48R6HVt/Lp25Xt1FR0RCSedP/b2TWh5ObiOg6fjDKLNVNWN/AsxK95A1n6EnnsuRsFIpFMDaip61omxiri885BqZtJqPLdmC0bPkbiOib7fuZiBwyh74xmsXqfGXxfMzie/bXvCVWUMvWYG2x+8IiG4UPvSXzlm2Eh0aVEfqeOCWx5M0Hp65MU3ufHiwYxZspaiqihldRYj+rcjI6CTH/TgNx2QCqqrJiXWPYJteXjELdSZ9Zx//8QWgXhskpZro5Bg9bsjfqic3doLz8RCEbtNwG1vRish341QHTtuE1YYUHFqFXoPHMwxw0YQTM+mprKMd557BKEoBA8ehMSIZ8OjGyYDoASPQm8zFk/n2QgUJA5WyT9wa1fg7TIv7uMcW9QSD2syXIy0qjHfuyqhpM0NrUGGizFy2icoR2cOm82y/yujfEMt/9pSx8DrD2JjKMSL733DZQ++Q2VVFSVVYaYt+YxxZ+dQmL5zwtyIn0K0HUeClgqNQb0ffe/kfbat+OmQrotT8Rp69klI0lAML1KKBl9ciW2q/OfRbWz5spysjkGOGz0Dc2PTZiq0tJjYW8O8hab+7ahUeWbqKGpNG9P1kKFHkB9fj/BmNm3+KAhfBzwdphPd2Nx3dyYCk8j6xIy8te2B+Iau556LueEmlJSD8XSehUBF4iKdehRhEV3eXHl1Bq7qw177DNRuxPVdgeLLQXgy0TsPSShhbxRJgthhQtG9pOe1TVrxsb0yTGpmHo9dd0Y88HTxgv9j0cuxVp62uRk4UlBnSQJqzNnAdn9+mdmOc39H39JW7F2INGTI9aiBmqXiug6rqyBkQy8B+R4DqIedEHIp/Ai37lcccStUVRDQBUQSSUrlu4/jv2QWBPIBNaHiTUv/I9a2h9FzhuLtPBdwce0Q0qmNC2U2tgjF/99QfSS1TCQKipGLRMOq34Dh82B0GQyug3Qcyl59GLP4ezz5+1H83LUtyorrtRx8GYGYMJxfA6sOp6YcIEHsSzTLpjf3LzdLi1EzCn9UJMxV9N9E2XtPIFnm2Og7AcsMJewVe2uwoRGKkKhdhia0Mhh9JyBF0zlsV77XMH78wYIW88foN5WSpQ+Sc9YkBFGs8rea7dcOVtmraKkHES15OH6OEHo+3v1uJ9L4uM2VSClakHlPxxmYn96O0fMqMCtx3AAlS+aTd/5s8AYZc+9LfPL6C5x07uWUP34NdmgbKVc+lDS4kJadz5ZQLWpAZ8GYc5j15FJWrN3E+Lv+zrQRp6MLyAoYFFVFWVVUwyP/2cyI/u3IDhjUC4E3aqN7tKTEOjc9k4ga4dQ7rkoaiPeLFI4a1YsP7l+d0ENuix8+C7sOdA605/UrH0UqLlHVxCO8e52gWysSsUur5YQJExLK1Ovr65k4cWLCa+bMmbN7R7YXwjYlRCMcWeClOlzPv75Yz/njb6Pk6YnUNpTIXHDjHRCupvyVBbS59vEW5blGzhkIooS/uajlB8gmChqLGCaqngtfHjIS6/1u7OHRe4/GXD4F4cujqDLC9uOmUZARIBgMUm7qSGKHurqKCLW2ycK/v8rUc4+Nk/GFL37Mim838d/121h862j0XZgyP5Vot+LXR13ERzDz+LjqaWPJuaumU1eVRaTWaSDjqfQ4oQMOSkLW2iz5G572fyG66bYdfj+T74ssRs55mC0lIU46vCd3XX4CxmEzMJdPic9Fo+8ErM9uAU8G3j63IyPFSMdCCh0hW2bkY5nzVIw2o1G8nZHWdpzQPxN8xn37PwmoGEcuRLgNoliKB3vNw1C7Eb37pZgrbmt2+JiU8DcRvry4YJLWeyKlGzfiz23PpbcviQsVZhZ2YMTC/6NGMXhx5RZG3PcGhiLRDQ8vfvY9z771adxvPIJGdZ2JkWLgcwSGksZJvf7Aq6vfj3/m3lpm1opfhqgdBQlaVEP1wudV8H9bAARf5ahc6NFx0WJaITLJIUv4EE5Fy+dbscdgKyYlJVVY1WVkZhSgBrNIP/oiVH8qLhqKFgRsoqVLmrQutIyEVptGeLvcieLvgZ53IYpRiGuVoASPwsi/EJQg0i4FxUd03TWJJGTDG+hZB2KumIXRbyqp/c9E0b2Uv3p38rJix9dUMu6CqvmTin3V05RNV70+3EgYx9ap+vApsk6bgJnS9gdFwsL4W5D2/IvmU7cHlb33FJI5zzQ/S0FD9cBeGGxoDoGMk3Fo1vd95O3x1yT7XncMxjiOJOrJxUcKxoCFsXY4VELv/o2MY0dQu+ItUvsdhVP+N5zyvyWMwcg5FU+nWVjFsRa0WDVorF1H6PmAghAS19YwskajpHXGrfoeZLCh+i0N69u/oXe/goJL76L8n4uo/+pd9IwCTr90FlVvP5JQyeLvcTTBfoNQ/ak49dXUfPYKKVkFKJUuI+c8zm2jzmDiBScz56l/smLtJrLSUtgQinDtMZ244531AIw5uiPT/rk23j52+9AetFFpQaz/MKo3KAphK7rTQLwjXdSAyrHjDoplxJuprO8Mqqog6xzevH9lAolv1VDc+7FLK0qHDh0SHl9xxRV7ZDB7OwSg6QrR8npsj8OpB3ei5InRCRulU1lE2QuzY2U/qoXrpuHpMg8hXUDg2iFA2Ykia138/562NyJtT7xMqHlGvBEyXIzQg/G+8JKKes649RkglsG7bdQZtD++gIqHaghkehFIxh7fnUdHDUwQobvpH7Di202xPthWWef/CdiOpCaST7DznSjCAhRcqbHqn+Vkd46iaII2B2ax/zHt+OadzRj+dmS3zUoIHsmsQXjaxyzTECquFISjMHLOg3Fl8cuP7wN2HfgzMA6ehPBk4dYVNfU/HjgWHDCXz0LvcxXm+hsw2k1M2p+OlFjlr+HZqQOBg7n1foz8C3FdDXvlfPSeV6K3PQ7phJMcPmIl6ubHN8YJujTSsKurMW2BEkgDFD775zMJFiqv3TeTAcNGcmzndKb97V0uOeWPPLr0DaZdOphjD+kNQiEiNTyaQl3UpLbS5PN/fEuXUzsxY8hNTBtis2zth/Tf7wg01UBXFHTVxTJbWfm+gqhl4sOHcAXCK3i3FAoDCr2Cgn9vd/CnpGITBZmczEjFB1ZrhvzXhG0LLpv1KLnpQR6+ci4pbi0lz03HDm2j09SluJFa8KZh5A+Pu0F4Os1Kulah52K0GZewnno6TsMqewG3dkWs/UYS67FtOKmbG95AbvwnMrVTbP36bDp0GMW2JfPJOXMyTm15vBR9Z+XiOxP7ipMqVcepKcMJR6n54FHSjjyb8pfmkn78yJiQlysTfq/5++6o7G2kZ1NdvvfNUUXaSfuhhRGz5BK+PPRDZ1Jj+tjbgg3N0SgCnPBcuBikg6oKHEcm/V6TqawbtVvY1oy0541YRMpBJ1HzxaukHTYY6WrJ74OGYKPediIIA+mGEdhNmjRCR9q1YFVgrbwHz5ELY/8eMTsW5F+/FLXTmURLt1P6/PSEs3bpMzeQNeg66r96F4C6Ne+TcdylFD81qWmcF8wmZEqEEOSmB/F7DMYuepZpI05n2iMvkpMR5OZXN1Feb3HPWb2QwNbKSDxjXlQVZWyD/e5DK55h9JjzsWsdIjUWq5Z+T69TOuELen+w39txmmnB7EKCW5OCtxuIPyQKwbWeIPZu7BIhv/rqq/f0OP4nIAHHlkgBiqLgFYKqHUpkFMOHHdqGp32vmI2JkES33oeedSJCDYJ0kwtmtf8LUk3D2+VOhJGLG64HpzJGcrzZIDTMVXfFS6qgQXFSy2Bb2gW8+JcxDBx/b/xnW0pC+D0G/kwfh47pQ06GB8euZPHN57UQoRs3+W/c+EQNmqrs/NpdiUdRdznK14rfHrYjCdV4UVU/Pk3DsV06HOph1dLv6XliR/oN60p9KEqvP3di9b/W0/+Ctjv0dt8VUwZuEB5U9GwMVSTYfOWkejGCabj1xQgJ5n/vR+twUkP/YxquWYe0wxiHTQcRjTkMbH8Ive2Eps9yI0jFg1V0N542V+JKrUVliafdBMyihzByhsSsh9IGIyMVMRX1lA4QrUh6+HD9bfAc9xSu62K5Amm6OFoq5WVbySjsRNQyeeuxeS3+dsdcOI6nbzqPqfe/xdX3vMyKtZu46qyTcTUvqgKXPPU5hWkebj31ADxA4ZlduObVWFT92K6ZjDryKK58rplA4ZAe5PvVVlK+jyBim6TJmAd5qYCyKPw5TyNfMekUcfD4VSzpRd3ZgV/4ELIegUOr8eOehyslpu2wpSTElpIQxXU29a80EQCkA+HtCF8wwQ1CSrdluW27SUg3jLX98R2Uo6fh7TwXN/2PSGmjyCgyUo+5fFqzFpzpWN+/gJLRHa3ruSiBDuQOuZ7KZX8n/eiLKH5yAvDzysVN08VR00lJi/1esN8gKl67h+im1Zjb1v6oQNuOZN+n7Py88HuGK7Sk/dB4czAGPo0rNGp+5yrruwKX5NdpFW/A582KK6n/mGJ7MuG34keuIe/Kx0jrfwZutBbqlaT3QXTzX2OZ8XaTkHou1vZH8bQZjdF2bCzo6NSDU4bQPBh9J+Ha9RiHTce16hHeTNT2gyh7cSHpR52XtBxdTcmIPw50/0OcjMd7pWhbAAAgAElEQVTH+dQkCkbez+QnXmXs2SdQWVvPlpIQWWkpPDDpYmzFy6ptMa2OyojNxU+upDDNw7STu3HnuxtYVVRDUVWUiGNxTMfDeO/OVQll65Vb6jjuur67t9/b5QeF4Fqx92KXVsz169dzzjnncPDBBzN8+HA2b968p8e103GcffbZnHjiiZx99tls2LDhNxnHziDdGAmt8WmUhEFosf6b5nDNcKx/+8SLkdLGlQpGzhmYW+8msm4sSBNz/Y0xgaw2o/F2uT22OEkbYZcSWTcGadVCuATFlwu+AooXL8SsC6MfcFFs46BhYe12LXdffRZ3XDaI6tLtVNQ22Sa1zc2gPmpSUm9j+erwBWoIBlRSc/ITxltRtJHcNB8P3XAJXlVPet2qqlBZVMvbC7/glan/4e2FXyDrHNQfIPCt+P3AcVx8qR7Cjg0Bhb7DuuLL8GDWO0gXPnhwNZ37F/LxU1sQWhbRTbMayHAFnnYTsMpfQzqVIC0UNTa3GlFaHQHpIHCx1jyI1uGkWA+5WYm5YhZC2lC/Fem6CE92XFMBNwrSir2JtLCK7sKt+QBp16C4YVBS8XSehbfLnRhtRsfEkWo+ILp5LnrWiaB5MPpOwN74Kqh6zBqo4d5ohPDlEbYVVm8IseGbb5l99pHcMLA98y8+Ftu2CNsuW8sqySxMrBDKLOxAfVUFFUUbCVVWsWLtJtrmZhAKO0xZ+g1ZgZhdR1FVlJte/hp8OpMayDjAab3zuW7JV/HHRVVRxi35iqhs7QvfVxC1o6QSI+TrGg5l+Q36UIeGLYJGhNLwzslPXOxN1u/RcbYihoo6C4mIr21+XSQSAKFgb3wVgR1bhzZMJrJubKxPXAlitB0b28vbjMbc/iDm+hti61QzSGs70q5EaFkgHdzarVhfLkI/cCzegY9jHDQe6+sn0DoOQu9+Kdaqu4m8fRFsvJ/s489Dy4ydNbSMAvJG3othuGR5q8n01eHz7tpe7DgS6TgU3XsZxU9O+NGM+/8i6kwf+qEzE85S+qEzqY4GCEVSqAp793oyDsmvU+s1nrJXH6DkyfExO7NdwM6E34QdpVakI72ZSFSkmoWn82x8BzyN0XYs5vYHY7aq1naim2ejIDFyzogVBUkLq2gRwqmIWQB6cpCeDITmQwoV69PpOI6DYytknDQGNb0Af4+jE8agZRQg/OnxM7iakpF0nNgmM4f0pVu7XO5Z/BZtczPISEvF1lPYFIoR38I0D6G62HmkqCrKtH+uZUT/dvGfCVz2z+uclCi7jqR9alsWX30ny25+lsVX3/mjzio//AdvEH5rhrgQXCv2auzSVzhz5kzatm3LwoULyc3N5a9//eueHldSTJ06lfPOO4/XXnuN8847jylTpvwm49gZhBC4QZ2yOosnlm/BMtLIvmBufEHQMgpQM9uQe/YMtLRMrJK/I4SC1NLjG7ZiFMT8mBuE3iLrxhJdfwNC6HFvaISOEihERkqRQiXrlHEgXcxQLfoRC3CPfhL3kNn8Y+FsNqz8OCY4dfsSnnr/v0CMMC0Ycw5tc7PoFqyk09pJOG+dj/Of67jstjvp2Ofw+DVlFnYgLzuLtpnpOxV006SIewhCUwmNtgPBUFUFR9eIqiqOrrUS9t8hHMcl6jo4jsuqpd/jSzWoq4hgBDS2fFnOmncF3i4Lmohw6RKMnCFY5a/hWiUg3Ya5FTu4PvjGShAq0o4gIxWYy6cQ/XAc5vIpyEgFwpOG/e2z2Gufwo1G8LT9S2yO26XxIFV0w+SYErGeH+utFAJZsx7sGiLrxsR/Do0qxemgBeMq6q4rsS2J0W9q4uHjkGlEHC+m4iOYVcCwm+9hzKPvMOzme/Bn5FMRFeTkt2HEHS/ESXmjxdkbj8yOib3VRuM94499WkxRVbRZWWds83YFCR7iqT4t4XHj6+zWCPc+g4jVlCHfbEqCusDToBGSLRyytDq21vqbbIN2gFRihLxVaf3Xgem4lNQ68bWttDqSEGyXUqB3GQqoDb2vTRCYRNffkLCWxdep5q/T8xF6NhKBomVgrXkCN7QG8+MbiX40EemEcYuXoXgyWrTf2Kvnoaem02bME+QNn4su6jGXjSPy1nCs1XfhF5Vk+mpJ80XQ1B8O/DUKeTXH3irQ9nNgO5IamY86YBHGwKdRByyiRu7dFmfJ0HidxoCFaIfMhw6jKF5yJ9FNq39SAOaH5ovjSGw9gKJpEK1FIEA6RNffEN+zgQbLX4HU0jE3z4wH/GN2rJlIqWFv/BdYtdhrHo2Vq1dVU/zgKLbOP5NtD4wiY+ClcVKuZRSQNmQKoepa5LFjSBl+J9KblnScdlUx5gtT8LthSiprmD92ONP+vZHMgMEj/9kcz4g/8p+mRGRRVZRUn0ZhmodbTunEHf++Hwdnp0TZMl08jp+ATMXj+H+R+JotJEeN6hX/rF0VgttXMXDgQE466SROO+00/vSnP3HllVeyYsWK33pYSbFLjOi///0vt956K0cffTQzZszgyy+/3NPjaoHy8nK++uorBg0aBMCgQYP46quvqKj4/QjbaIbAdOHpT7Zw7iFt+D4UpcQTE0MpGP8CFSfdgtR8lP/zDhQjiFu7AqxS7PLXUIw2gBq3mGqORlVrq/w1PB1nYK16gMhbF2GumI0wQ5QvXYiCoPK9p7BKtiKElzJyOHHiXUxaup6rn/iI7E69uHXUmXx43194buZVqL5UkFE8K6clbOza1ws44/pbgBj5GHrLM0x67DW2VFSi7mwj34USGlVVKDFdLnnmS065/1MueeZLSky3lZT/XuFC5/6F1JSFCWR6MetsAplevnx5E9+vcBB6FkJNj9mTlC7ByB2GVfoC9Y5Om7wcXpx1NV8/PIE7Ljse09XBH+vRbk6IjcOmY659FhmpQO8yFHvdc8iog7fzQoSnG56OM+L3QmNZulX+Gig64EHomcnvFT0TaUZj/uLrFiNcB7emgvK3/4HoMgbjmMeoKLiCx6ffQGV1NflZ6dTpGWR26knUk06xyGTKc8uwHZer5j+Fktme0+a8xE2vrGXYzffELc4uXLCY3MK2PDn1Cu5dXs6qbbUUpnkorjHj4ylM86C4ksK0Jh316rCd8LjxdVrrrbDPIGqbpJIKwJaopF2KimnHCLljQLoeoTQS4OvynbxBgz+5kK2E/NeAoSo8+0UJfn+A20adQSAjl+zzm4LtTk0Z1rrFSFQ87SYlrFtS6DtZp7IS17eO03CdCNb2x3DripGhr+Ovb64Hg5TJ22/qQ7iRWrSAH/OTqTEl8Izu6F2GEl02nuib5+OuvoM0o5qMHyDnjUJezRMJTUJe+wZsR1IV9v5PZcSTwXYkpqmw7ckpFD0yvoUd3q6g+XzxtO9Fm5GLaHvVvaiGICWgoFdtwin7Dus/N+DWleDaFUnvB4SGXf5aPPjuaTcJ9DxcGdsrjS6DkZ4stI6ngh5Ei5Ynlp8/PYngn8fhv/JZskfczfP33MazUy+j3rSRKVlsrrZaJMhyz5pOxb/vxQ5tw6cJbrnmEu5dXk55vYVXU5h92gE8dHZvnv10K6uKauLjLUzzkB8wWHTsfgTcOob0O56/vLKAXsPb73Gi7DguokEIbtD0/hw77iBEQG1tEf0BLFq0iJdeeonXX3+dIUOGMHLkSFauXPlbD6sFhNxZCL4ZDj744ISIwmGHHcby5cv36MB2xOrVq5k0aRJLly6NP3fyySczd+5cevbs+auOZWeoq4gQEpK1JXXMfXMdWQGDSX/qTKrmYkUtdF0hS7Mpf+hy1GAWBRfdgFnyVKzvtXRJQ6ltPkLIFhYPUslAES5uuAbC27G/fRY3tCa2QXcYBZ50pG1S9eHfyTp1PJvcNFwp2FrrUBlxOXG/FPy6gnQlpTVRLnp6JQ8OySdz+SUtrsMY+CTfbChLUFlvm5vBS7OvJTcj2OL14eoo/57zWQvLhxMm9ot7C5bVmgx/4vOEzGBhmocnL+xLdoqxB76NVvwShKuj1JaGWfF/39HntM58885m9j+mHR8/9TV1FREOHtaJrgM8YJUjncoYUc65iI/LYhvt4UZRgjJr3mV3o/u8CFyQTqztwghCQ5938/ms9x4NgF2xGmO/U2OR8obPMLKHIWUq1sd/QXQ4Gb3d4TvcKzOx1r+J+90/YqT/0GmYtTY4FmpqLsWb1vHc3Als/PJjAG7613pkMJeS8gpu//u/GXbsoWSlpZCZGuDexW/xwarvWDDmHFJ8Hl7+YAUXD+wTdyB46v3/cu1ZJ/HI5yHe/raCwjQP84f04IEPN8Yf33FGT9ql6GyqMRm7+KtmPeQd4mXrjT3kXXMDrQGqfQT/WP4v3nviSw7XD2V+rsbA9l72D8QIuVxTzfknzebp9QMo0U/hpmNbeh274e8xi+7E03kuWvDQX3v4+xxsx+G70nru+2Ajo4/IR1dAUxWy1DDV2zZh2jY5BeloKuD1gFsR179AzUcIK1HzosN0pJuOW7cdLSMbpMS1SrBKF2PkXYC1+inc7cviny98eegHjkV40xGan+iyiXFSrmR0R+t2IcKXj1W2FT2/C9E3zgXAOGwG1qq7m8h590sTbK48h9+CSO2EEInrjnRdnLoQ0jYRmoEayED8wp5wKV2IViJdC6Ho4Elv8bmt+PUhXRez+Du2P359gkK+kbdf/Dv/se9Oui5OuAphlcWq35rZjpa8dB9Zx12Au+4htO6XQSAd4dYkOrR0nI5Vsg49/8DYGaFR4FgJAkFwIuCa4FqY712Jd+Cj2NUVbF50ecK16Jc/xaZ6lbWbijj5oE6Uh0LxvfqSU/7Ii+99xs1nDsCIVODUhqh893Gim1ajZRRgnXMPZzy7nsI0DwuG9ED5vortq8rpd1Y3tpp2fP8uTPMw+6RuFD2/jooN1fxpysGc8sBItlRsp1/7nkw67nLygtnkpmeSmh5AKK2taD8GKSVRR+JKUAR4VJHg7PVLMHDgQO677z66desWf27evHls2rSJRYsW7ZbP2F3YpRCYaZrccccd8ceRSCThMcC11167e0e2B1BeXovr7plIZ05OENdxUQVkBPS4AuPs17/nlhPbU1pZxUFtUhFCUDjyfpy6EGbIwVNwJa5dj6dgJAgFt2YL1pb3MNqORUkpRNaXYH56O0RCOyhCN3mMa6mZmOXllL+ygKxB1+E4Dmc89CmFaR7mDO6BV1NxbIfSypjiqaYqLBzag+pIiKwkoh42OidOfiLh+raUhIhETUpLa9gRqqpwzJgDiZaVYXgkZlRgZGURtm1qS2PZwqiqJi3TjZg2paXRFu/5c/7+ycb2a2DHz87JaRm0+DnYk/N1R+x4Daqq4E01iFSbrHzpe3qc0AHdr3LM1X1QVIFUoDYi8Xs9mCKbcPYBrC7zEopI/pAdoeThJpEXNZiFrCphy0PTyR1yPWy8P+GA2HhgbFQ6t9Y8CEYG2v4X4obDEK1EeFLRAycioy7W53+JeZpn9sRa9QRGx9Gxg6onE7O6FC2zF+Koo8CThRUqp+TpG+MHjZTTboxfY2ZhBxTNYPjTqzisfRozLhtC1LLZsK2MmY++REllDfOuPpuHX3mPUYOP5aKT/4BEIc2Xhj/b5eb99ifg9zHmmHQuG9CedL+O5bhMOL4L44/vggYYrkttvUmeT+Wx8w7EdsFyovznuw+458zDURUDV9qkeBwqKnZdkXh3zvff43z9Le/nXwOlFdWkyiChgEACAeESDsd6FD1GFEVxkVLhg/UWlX1b/k2FreIBakIl2JHf99/p9zhX4afNV+mxsO0KJv1pP5AudjTC2s3F9CpI4b7rz6WiaCMd+xzOWTfMI69dWzABXYAFaA5S92PkjEXoPqQVRtoGFa/fT/i7T7DPXEhhvg/D1dEDJ2KtfQm92wWYVeuaEZtpSNsEPRXzmycx+k7A/HxubB3sfjnm57PjryVrBkreANziZTG9job9Xet6botS9+jHN6MPuJ1qM5CkJc0ADFQp8FWWobgWrqK3UNneGZp/73Fv708mx8epHzrzNysF/7XWl99ivv6ca1M9hQlK6nX44wr5P/bdqarARz2GIeNkHBqcTJZPIff0uUhAbZineDPQe10RcxBwLWS0ChlxkWufxNyUgd79QpSUQoTpIjUlxlIipdjr/i+mQePLQ0ZDuJFE218to4DM9DQqselYkIsSyCDNSKFjockhXQvZWFpNqKaeUstDoe6n/JXJ8XNB2rlzCadn8+SFaZTVmtz/4UauPrCQ/Y/x8+aCz/GkGiw6dT98OT4iFRG+ayDjgUwvUrhxBfXPNv2Xsx4dC8Cym5/FLP9lc/vX3gd353zdVUgpqY66/GdLPfWWxK8L+rf1k+pRdhsp3xF9+vThrbfe2iPv/UuwS4T81FNPZfv2Jsn+U045JeHxr4GCggKKi4txHAdVVXEch5KSEgoKCn78l38lCEVQ+WUJ2QflUpjmoagqyqqiGjyGxgEpUSpfnkPakWdT/PzMpszhRQtRAwFwFaIfjosvZuamV+PZQoFA6z0aJVCIcdgM7G+fjftiWqvuRvGlU/nugriqpN3QiVBUFWXiC1/x6AUHYYWbymgdxyXXUJD+HLTDZmIvT1xoQ5aHtrkZCWrZbXMzfkBlXeJ1iqh8+XpqGq4rdfh86imMv0JTRPxv0ohYma5o9WD+HcJxXFRd5Q+jevP+/at4/4FV8RIsYTSUR5ngOF6EN43vqiL0CNThDdh4NYVIMCtOyNOPvihuD1T+xmPknTkJe+Vs3NAarHWLMY6YQ8yaQMMVBurBk9lcZZMVdUkJKJgr74nfF8Yf70XvPRol2CGeJTIbMknCl4fniFlEP5qNftCN2HVh6jeuTihrq33pr5x25U08PXMMZ8x6njJSyPLrnNDZh+O6PPPvjzj/xCOYfMlplFfV8ujS97l00B/JSPHj8/mY+dq6ePZ73pAePPb+BpZvrGLu4O5UhS0q6iw8mkJhmoHfFeAKNEXFdiSK4xAwFDbVFbPg9UfZ8ve/xhVX0422WK0yqfsMonaUFAJUNohtpWjE10FPSsMaaQuK62BbraQgRRCtq2PxnGlUlRQzdPx1tE+H0nANUpVkGq1ZmD0J0za54bl53HvxAm586Rvmn9qFttnpqCqMuH0xj4wdyoaVH/PQ9RdwxX2vkpbdGV3YSFUhYmu89V2EEzp3xKM4gE35648T/u4TcodNpVaxmfLY+9x23sHYDWudpaXiGTAPGQ0hzUqsr59EP+Ai3GgEWfY5Vu1G9APHxtbBZueGRhLkGTCPaPU6pFXTZI3ajJw3QoaLcWuK8btenMyOuEJBwUW3q1GkhVR0pGXjVG7Hqg1R89krTRZoP4FIJ/P2tj6ZTGDAIqrCLStAWvHr4oeU1H/ouws7fnx2KKakrgViAaLeo2NzzarB/vZZnNpSpCtx1zQEg8LFmO+PjZ9vzeVTYsH5A8eiBNsBCtIVCE8+1vcv4H7/j6Yk1LrFGP1uRAovGFqsB7zhzOkdegvnzfkHORlBJl98Go5jkx4tpuzpidihbQQzCrh9+DzmfVHBurIwU8+5hzZBlahU+a5Wx1dvMfyJL+LXff3Rnfj4jpV4Uw16nNABQ1Owt9eT6lXjZHzAyB6sr9v8g7ZmrfhhRB0ZJ+MA9Vbs8dEdA3i1PbOv7UJh+G+CXSLkv5WIW3NkZWXRvXt3XnnlFU4//XReeeUVunfvTmZm5m89tDhUTZDbOYP1b29mweDuXPfCGoqqoqSLKCV/v5GsQddR2kDGPe17kXni1Wh+D4oicaVE73MVwpOKjFZjffMsMvQ1wpPZosysMTsujHSMQ6dT9dm/42U3aiCDSqvpYF9UFY35HO7QX+I4LjgutWo+gQGL4pHRGtOHCjx0wyVcNutRtpSEaJubEVdZT7YJ+6hn+xPXJ/bzPHl9gk2K4bosHNqDcc3KfhYO7YHhuq1r1u8UpuWgBhSOHXfQTu3sHMfFsKIcpBZR/EhTQCZ32FTKX70rNi/T88gadB2qPxWnvhonqiK6jEH1+nBqq9j2xGScmnKyB09Cy+3GFlslyyOofOgyaoJZFJw3HfOTqQhvJgIwV92NcfANsfsh4wD0/c+N3zdS8SK6jWf7cwtwasopHHkfoZfn4Wnfi6zjL0ZNSUNNK+Dse9/nlvdKGeE3ufzwfK5f+AiP3ngpxxzcnVsee5lrh/2J7h0LmHbpYHRV5aWvKqg1a5hwfBeuP64LrpT4dIVTe+dz9H7ZzHp9XbzH7LHhfdCkwtsLP6euIhIPZKgBFct044qrtmujKVrM/uQXiLy0Yu9D1DYJyDQ26AJcSFFdrIaF0OuPBU/1hnX8s22SQV0F/7p3Ad8uXwZC8Nxtt3L9nK7UW7Wsr4Y+aTFhuFbsGRiaQU4wE1UIyutMpKLird/OXSOH0K3/cVz3yBsYhoGraCx4+RPufeG9+L753JpKXviyhOcLU7j/jEI8uk3mSRdjhc5AOHWkeUymnnckjh5AP2w2brgSp7qCiveeJ+OoM5GOitb9CuzqEhTdg37oX3GtCHZNBUaKk5xkm2H0Q25DePwxAazlUxLIeSOELw+nuoKSJfMJXPowm0w/Bwa2YS2fgt1w3tB6T6R86R04NeXknDmZyjceIP3UST9ogbYjdubtva8ot+/N2Nl3p2GRIkJYlZtwpYHlRFtUaxh9J2HVOSi6knSeCiMdkXEAWvfhKIEC7OpS3IiF4jFQU/PROpyOzDsaxRNAqjpG79G4aGx/bAJGXmfajFyE0DRQdMqiBndfP5zK2nrOmXofd1z+Z/LfnJZQqSdqS5lwWAe+rvAy/Z1SZp56AN+X1/PSqiJO693U116Y5kETAm+qQZ/TOsdb9QKZXv4wqjen33JELGShQwe3gIdH3MKlj9y8e2zN9jG4kjgZb0S9FStf31NYtWoVXbt23XMf8DPxs2Qza2trCYVCCc+1a9dutwzohzBt2jRuuOEG7rnnHlJTU5k9e/Ye/8yfAseRfPDgauoqImR+V81DI3rjKCCcUCx77U+Nk/Hs08ajahbWxxPBm4HR7xqi5Xc39dT0mYD19RKEHiD66czECPjncxv6ybKpeOfveDscGMu2nz+LquUvUNTltPiYCtM8/JCoaqN4SRNid0FhehqLbx2N7bhoqrJTMg47t71ovtk2ZuUfPe9AbFeiKSJGxlsXrN81HKdZwGQnX5Xh1FG8Q0Cm5LnpZA+eROjNh0BC+SsL4pHsghF3svnultoFiuGjsr6CcsuLT4vZCqnBLKSWiueohSCJZ4OkVYOSPwD9gCFEt85tum+CM1D9vvg4kC6e9r3IGzIGe/U87HAxji+PTv2m0zXHR1t/mI7pKneOPpnK2lhGfNixh1IfMSkqq+S5tz/h3FNP5O4PYgqrr39Tzj1n92bwA5+ycGgP5r65rkXVR3bA4Ou/r23hOnDsuINwaFBcxY8HwKF1094HEbVNcglQrQhSVBDNTh8eX4MWR51KukfySZHkCP9mVr39bw4//QyC2Zm88fCD2G530o0aFKA4CsHkrpSt2A3ITkln2uDR1NeWMX9ID2RdiEfHDiGYnc8JZ19KxZPj4uvbiMEz+Pir9qxYu4nLZj3KLddcwvdl9dw+yA+bxhJp1kdurX4Je/syVF8e+mEzsCM2wo2tuJH1n1NStoncoRMQ2KBb2GsfRkYq4vZUBcNvSUqy7eoKiv82k+zBk9DzuiC6jMFVMzEOmYr56fQ4YdJ6jad4yZ3YoW14VcmBadVYyxLLju1Vc8g6fhRFj4yn9PmZZA267icT6Z15e+8ryu17M3b63dVujrdQeg6ZhhOOYn4+a4ez6mxEt3E4tcmDQdKTjXHI2JgOTFHDfdFmEubK+3CNTPSeo5BCQboubrQKu6YCNbsLABkDTsH69C/xuZzWbxrlIofLZz/GlpIQOane+JkklgC7Kp4Qy84oYP5ZsymOWMx9cx3zh/Tg1a9iY2tMFlEeodfJHeNkHGJ7+fv3r+LgM7uy4vlvOWpUL/SAj3bBNq1B9p8JRcRsJJuTcr8u2FOt92+88QbPPvssDz/88J75gF+An7Qafvfdd4wfP56vv/4aIQRSyniN/5o1a/bIAJujS5cuPPfcc3v8c34uXEfGb9yKDdWseuQrOp/XFccfK61x6qvRMgpIP/oiMKux19yJDBdj9LmK6MZpMesHYhYQ0a1z8fZZgHSSRxaVQCEuKv79j0DLbEvWoOsIvfUIaceP5KmPYsq7vyQL7TgSHQ1dbXq80+tusL1oTsqTqXQ6jovquKgATms1z/8KdhaQ0XM6kXvuX9n2wMgEsm5VbEk+X8wwG+pqqdezqBepBHoeTfZJlyIi24l+PjueFQewv30W45BJRDZMTLxvNkzByBlL1vEXU7JkPiga2SeNxF49L+Gg4H42lRv/OA/no/HY4WJ6+fKI9LqJa848jlFzn4xXhswfO5x7lzfdf0VVUQw11n7xyH82M+3kbkz759qEqo8MBYq+TJTH3tF1oBX7NqKmiQ8vVQLSDYHtNk0OwxvrjTTrfHTLdFlTJvhs7YsIRaHnH/+IEAIjkEq43iXFX02qCqVR6ByQKHuo525fh6IoCBTssEnlfzaTM9BPRdFGzp98J7Uv/TVhfeOFKdw0bBpn3PoMW0pCZAdUrj86E1/xDYlr1capGB1HY25fFi811w8ci/lpjOTkDZsM0sRcNq5FdZy9eh7ZJ43BcVT0g27E+uKvTSS790TsCORdMAcnUkvVsn8Q7HMC2x6ZgBrMIv+8WbiRKpzqirjNlZZRgNeQiGhF0vOGlpIWvz41JeMnE+k600fw0Jkt+5BNH83Lo1vx+0Oy7y6u9UID8f50Gkb/WUkz6aquU7L0MfKHJc5To+8k0Fyi30/Z4ew7G0/P8eAqmB9NTJj7buk/UTIvJPuUq1rs6Xw2jeAh8+OtlqXVEfIbzhnpR18UJ+MQm8fhf0yiw+UPkRUwuH7JV9x1Vi+O3T+H7IBBpreKLZIAACAASURBVCawpUAoSlIHobQCPwef2ZXVS9fTd1hXoqbTGmT/mfCosZ7xHXvIPT9iz/hTcM0112AYBuFwmC5duvDAAw/Qp0+f3fb+uws/aVWdPn06hx9+OE888QTHHXccb731FvPnz6dv3757anx7FYQqCGR6E0i595UNpJ/dCe/QW6j56AlyzpyM0AwUXcYXL+FJjS9IjZDWdqRVjcCXPLIYLkF4cyl9fiYFIx/AKNif9FN7YqoBJh2vcP3AXy8LHcZP/kXzE1Q6m2xSWjfb/3XsLCDjCD0pWQ+9+RB5F8wh9OZDBPsNQkvLRU3NQVFdDnItUAykaaOddi1EtmF+eXs8K954L7ihNUi7Nul9I3QfaqqfvAvmUPftcoL7H0R0dZKDghmK34MyXIx39a3oHW7mtlFn0LEgG4+hM/edLaza1mQt1ah7cOupB3DTy19z57sb+MsJ+9Eh04d0LZ748Gmu+cMFCesANPmRtpLyVgA44di6WCUlOR4V123KOHq0GCG3agO06yRZvtVh1Tuv06XvIegeD1JKMnsOoL66koysWoIaVFpQ74hYL3or9gg0RaPaqqX0mxo6HOIjs7ADwfRsapMEI3NSY1VnbXMzCHoNPGoUWZVkrfKkNj0OFyM0b9P/3RqshrWv8Tnz87kYB40n+tEEjJz2OFLFrDZRuo1D1XWc2iqKF99B7jm34Go+NF8aKUecS1QNxEW7bMUDbpSSJfObtGwumIOsD4FTmTwbalnkDZ+LmpKBlppLvRqAn3CusB1JTZL2uP9VO7H/Jez43SmKwPxsJm6oKQknw8UI1UjeElFbhVNTDv4GpwDNi7RqsNY8iN7niuR7eKCA6HvjW1aG9h6N+clUjP6ziCYh/16duP7Rrc99wP0jZsALU+LVqQnXFdpGdV2YEf3bMW7xV+hCkFJjkxnw8OUL37P+o+0cN7Zv0r28als9K57/lsMvOKA1CPoLIYQg1aNwdMfAHlFZ/z2Kt+0MP8lz4uuvv2b8+PGkpqYipSQYDDJx4sQWiuv7KoSA/hd2T/Ah7D6wPagGNf58tve5kAotEyU1FzcSjnsyy2h1Ul9GWV+Ka0da+jf3nYD1/RLsmlgpvHQlekYBtY4P03RRLRuP46Ba9s8i46qq4OgaUVXF0bUftWJyHImRtx+5Ix+m7cSXyB358E8WfWnF3osf8q1tJOvN4dSUo6TlkXH8SMpfWUDZS3MR0e2Yy8bhvH0R7rKxaLIaoXtim3fzrHjfCT9+31hh1GAuZlUJ5f83E6tsa/x34q/z5SHNyoTnZLiYTvkZuJ5Upvx7M5OWrufcQ9rEPcMbbc0EUJhicM+f92f60Z1pl6Zy1ePXM+yeyxnS7zhQBEeN6rXH/UhbsffCjbhIoNKhRe93qGorAB9urKNdUBKs+Ia6ijL2O+RQpJS4KAR7DaS+KowbKcPXUMVUa7fOrz0JLz4ys9I4+OIufPduNSPueJFwXU2L9U3LKKC0OkLb3Azun3QxwYCf7GAg+VoVrW567MuDZoGZ5mtfI2LZwlyUvAGAYPNtJ1P6/HRcG0peuIOiR8bj1JTjCIMa06DaDcbPBbWOj2o3SI1pUO8rjO/XBaMeJPTmQ9ihYuyNryassY3WVXgzKX9lAUX3XkbRA6Mwareg/sQM1r7i7f2/iObfnS1VZKQi4efCl4ddX4dx2IwWc0fN70HBiEUI18T8+EaiH47DXD4lFlSPVCb3JJdu8rnfKEyoGMn3dGHw0A2X0DY3gxVrNzF5yedkXHo/anph0vu0uF6S6tMoTPMQLQmTqsbK49d/tJ1AphdPqs4fRvVO2Mv7X9Sdr/69kbqKCB8/9fVu+fvu6xBC4NUU/LqCV9tz6uq/d/ykeLrH48G2bXRdJyMjg6KiIlJTU6msrPzxX94H4NqSL15Yx8FndsUIaJh1Nl+8sI5+V/QiEExF1QwqIibS8ZATLIiVlq2ag/XNs3j6TYuXrcf6aGI95FqHU5G+3FgPrWuBdDE3/Au92wVYdQ7+nkejeVVkpIQ0n0LdL4w6q6pCiekybvHqhDLcXEP5QXIvFIVaJ7lKZyv+t+E4krCvTYJtSqM1Tlj1kzdiEU7FVhTDh2uGUTPb4No2xQ0+5XnnTI6XskGDiutnMzCOmIe0IwlZcWvNwzE11kABrlWLp+N0ohumNrtvJiFJx3EUSp+4Hi2jgIg3H/8h03E+nRovf1MPnYH9zeMJ1yF8eawP2Vz/8ob4c89+upW7zupFqN4ibDpk+DRW3Leaig1NB+lB0w/nnuFT0BQNLz5My0UNqD8ohteKfRxRQVSAKSGlmZJseXUNHoqoDqssKy/h+NpKMrZ/BkB+51jvpKmlk9bjQOq3PI4TLUcXoAmosiE/6Ye1YnfAcSSZeiZ2tkm/Yd2IaFCsZdDp/DmUNig5NwYjLZHFMzNGs+iDrYw6Kg0tkEJqh1swN97czHt5BtaqmL1oYwmvVD0oGd0bso8yeba6rgi95yisUAUFl9+HkZ2LEJL882dQ+tIdpB838ker0xxHElH9BDxhVGmS3v9Uala9R8aAIVjrFseUso100FMJfbCEwP5HojY4Z9ihbZQ8OT5BtLUV+w5cR4mfXZu3SUgjDdOTinbUXSjSwkVDCoFq12HVbMOt97SYz/aG1/D0nEZ0U+LZ160tS14Z2lAlF3YUjB32dO2wmWys91AvVBZNvIwsv4ZQVDZbKg+9u5FJw2bBczfE79OsC+ax+BuHQzo4LBzcg5Qaiy9eWMeAET0ZNL0/KCBVUHA55JxuaB4VO+qgak0JqrqKCG5rYKkVuwk/iZD369ePf/3rXwwdOpQTTzyRyy+/HMMw6N+//54a394FBSLVJu8/sCr+VCDTi2W7qLqCZnhQpcbI574i268z7+S25By5EKSLq3jxdl6AjJYjI5VYXy9B33840pUIq5rox03iFcahU7G2vIPediA5p4/BXDY2/rPgoTOpUX++t6epKHEyDrGe2XGLv+LR8w5EbSUUrdgJfsg2Bcui7IXZTeWRw+cj1KZSdj0jL2n5mRDgGtkYfSfFlVtlpALhzcC1owhFxVr1ZDMf8ixcV8cu2QSpPvInvAiKzs1vFVNWZ3Hj0bPplKlTFQVDCRDc/yJkdZPXr37YTPxWJoVp2+LBqPMPbcvUpWsprzNZOLQHW9/anEDGY6XoAp+b0qCLIBv+Hj8uhteKfRhRQXXD7utvtguv2biFHt1tLMuDV2i8+cn35JR8BrldMPxepCuJ6JkE2u5P5FsX4dYhBPhUqLV+m0vZl+A4EuHoSBVWP7uWnJM7ctPHVVwweBF5fkGK38d9K2v4Q1eN299ez6qiGr4pqeO+c3pjKoVktl+EojgIAVZNHVqHUxFdhsVLeGWkAr3vxFiGyJffpJC+Qw+5ftCNYPjQdRtr+aT4z3NPm4FNKj63nrC6c7/wuLf0sslxNfWMAeMJLVtK5sBLcaM1OGVllL8xj+im1dR9+QZZg66j+MkJQEvR1lbsOxBWhOLFd5B1/Ci0lLR4m0TOObdRa5qAgaZ6WniXa30mwUGTsb+Y2bTn7n8RlunD03EeQhG4Vd9jrnwYgcDoO6Glu9D25RgD5hMKmxRFAmi95tImqIKic+s7ZSz58vP4OF++rB+arjD6mS8pqopSVpvCX4bdTdugglA1Kl0vZ/czwIUNSzewoiErLh0XV0pcVaA5gnfu+rJFyfrBZ3aNW8G2tqK1YnfhJxHy5qXp1113HV27dqWuro4hQ4bs9oHtjbCFjPs2N1okHHl5T0ojDkEB22tMnv5kC1P+3I0umR7Sna1EP2zabPX+s1DUdNAEWvuTsL5chNb1XMxVdyf20nwyHc+RCzC/fxEtp+9u9fa0XZmgGg0xUm67EvWX/XlasQ9AVQU+6lFcC1fREQKKn2xpiVcw6sGmvnOhJI2Gg8SpLsdBxeg/B6GoIFTMVXeidTgpfl8k+JAfuQBqPkGkBrn53SgTj9uPoQep+AyV7WGbv79fxkmdfYxb+DC56UFuufhGurXJor7coaYshQ1vrGPhHzqjBnSC6QbhighT/9AJp84i9PpmOh2az9YVZQl2Zq2l6K34qVBMQXVDya9PleDGvFHXbNxC4REqYcugr6+QFV+v588lq6nqNRjZoMQe0bNQsZF40LUYKfIqUGqCK1uF3X4NWLgceEonvly6nuuG7keV7RA1VBRdoU9bNU7GIbZ/ltaaTH7lGxYM7s7Gp9Zy2NBcUgL1mMtvTHhfJaM7Qk+Jk3AlbwCeAXOQZg0yWoG1JqayLqWEuhLsdXe28CCnwyhKlswnb/g8oiltMZOoPSfzlrZXzyPYexRWdRVF916W8PpGl5hGJBNtbcW+AVfRcWrKKXpkfPy5HedD0vm1cjZqn9j89BZ2xSzfiuP60D065gdj8QyYg7Xynv9n78wDoyqv//3cdfYkkz0hbCoIgiAqUbBqWX6V1hWrbV2wCgqtooIiuFQR/bogILigFQUXxGq1iFZba61Kq4ggLsgiILIIZM8kmcx2198fN5lkyFChjVh0nn9gtnvfzJx3Oe97zuc4G+/QFg0X6IqNhImM0jWAtuJ6vLEqfJ4ihOPv4NHVAj86Ip+X11Yn71+a7SJaGcVT7E2uZ22gPmFzWYsKe6to64sbwow9qQulEZ0+P+7KJy9/yRE/6kKgxAsWaUXdVJ+cmf8zdDoHlEPeXiZeFEXOPvtsLrzwQp5//vlOb9ihiGlaqH6ZYdccw09uOI4RkwZh+BWuW7YByxa47fVN1DVrqIkGgmIT5kep5UX0lTdix+vA0tA/n48V2uiEjbmDqCfOwHXqXNQTZ4A7iB2vQ+k6HLJ6Igb7Jttgx6oQ+c93rmVRSObMttIqZJUhw79DkgQ8sd1ULxjHrllnU71gHKIZTxFTcXXrT+Ho65EUk+LfzMfb71T0+uo0OgnTsGI1iIrC7kcnsOuRKzFMBSQbpc/5iDndwR1Mub9zgl6HUjYMe/fbTB6aS5YZ5tjsOK5EA09+sJPhh2Uxea6jov7x5p387Oan+fENT1LZIGLbAnvW1vHxo5+zevbH6DsjrH3qC1bP/piPH/2cbR9Usundrxlx3SDOmHEiwyYfg+CTMqHoGQ4YSZNpbD0hb5mFm6IxGiNRCgI2EU2mj7uQQHgXgqWzK/f4ZF3WuJyLW69HUAIoqgCWhqtltzRuZcbpg4FpWuCTOPb8XuTIIoooMPGP6/iiMsKsf2xNOuPgzJ9NMYM9jQkWrNjBSb8uQ3aJSL68Dnmwcu9L2k7Eg32Qe56GbTaA24++5bmWE/RbsEwR0e1Jm2cr+bNbNj6n4DFCKbnekiTgl2JI6Gk/q3bpjZRdnDbf1tJiyf+3ibZm+KHx7zRjWhFtI71avy+AkF2CYYjYkpeKBb9FsAxnTSuC6+T7UIfejRDs40TDSS7iEYnaPSoNO5qSfaP1evZHt3FVeYD+vijPnteNASV+SrNd3PfTI9n+1x2YTVpyPXvp8UVcP29xUoV9V3WI6+ct5ow+Qaa9sZneo4/gs1e/YvfaOmSXlEw3a80fb8WX68aX687M/xk6nQPa4pw/fz7jxo3r8Pyjjz7KZZd1rCv8Q0SzLRRB4P2F6zlhTB+MPHfLCbNFvldh1skuEi9egz3mjvSiFe58tG2v4RpyLyBiy27UgVeQ2D2zXY3yadiajr7mLlxDZqL0HYe+caHjwHuKOMB9lhRUy2LuuUcxeemGlBzy/6R0WoYfFh6iVLfkhUNLibPancmT8Pb1wBNfOFEhuadPJ44fWTHbFFiNOLbkwti9HLlsFF0nLUL0FSDIDcS/ujWlH2ifPY4dcoRVWoXa9M/n4xpyL12A6qUziW5YTlawhMcunk2j25OckFvZVR1C8ooYCTNFUXXDmzs48ZK+rHxmY/JEvP/pPdGwMLEyYWoZ/mNkXaZKNpEFCVWw0IA9tY5QUsCtUxd2c7iaT1H8a2xBpL5gAPXabnJdFpqcjS+yG8mdAzSRqN+CK9gPgKhh4+3EcjEZ9k37tJQij8RTFw7EEmDWOX25YdnG5Px5+89689Dy7Qwo8TNtkEj9k1dghCrQThlDcOgdaKvbouREX5ekM64OHEdi96y28e64GUAOVqIZq7ke9FgyskgM9kXudQGCmoMt+nF1609i5zrMcC2eLJVmPG0bpoun0HXigrRRSYIo0Wz7KBwzm4Z/LCDnhDORsnKRfHnogpeyqa+maIRk+GEhSwI+NYaoZtFl4hOYhoBl0cEe9l27fDeqrwQrVoOFRGDw2diSinrctSS235Bi63ZMw1b8qCQQBAVvtoi9peOa2Q5XsXveWLKCJSwYM4doVldUE7pfdhSSKjL33L5MXrqRLJeYdu7PconsaUwQiejUbW/Cl+vGSJhJ7ZcfTejPe4+tS4mK04Rvv3pRhh8e++WQf/DBBwBYlsXKlSudcKkWdu3ahc/n+3ZadwhimhaSInHyhKP5/PWv6P3zXpRmu6gKa0w9KZfEi1djhCowmxvTD1iCglI2jMQHTs1l18n3k6ic2bFOY1FLSQhBTJaD0D+fj9x/Cqbxny/ITNOiUBV58sIBGNbBK52W4dBnnyXOxsyiavEN5I28NE098Bls7nYlK3du46qhI7ET9WDrGLuXo3QdjrbqJufkZujdJHbN69AP1L6T0FbcnJJf2dovMAwCx51BdMNyjFAFNc9OoeCKJ5JlUVopKwyCZuMv9KSknMSbNFxZCsOvG4SIgGlbGXG2DJ2CaiqE3ZClClgtR9+7a+tQZRmvEqfZ9KGKEsVmLVFvEabipSqu4vN7QBBR9XoUby7QRKRiIzl5LQ55Ztf0O0HXTERMUGWeWLGT237am7IcN9vrojy0fDuf7wmzaHRXEi9ekxwjG/+5GL1uJ/mj51Ld0Mie+gj9TbElt/aCpDMO7eqWF0xCMAXkrBKMhA/1+Onom59FOfzclHzbotFTqH3rOczmEEqg0DkVtzX0cA1SIA9bIG2Orm0LmKaN5i+j8KzfoK26DSNWhdlaN1xo1afJOOM/NNLpDiiD7yTaIiXpl2LJVLWY6cO/D/0DddCN6J/eg9D3RrKPOxU7shWtZl4HW3cfNg9twzPYtZ+QO/QhLMuLma6sWpOzkemIDV5PyYRFxC3HJ9ENg8ff/j2TThlJYZaSdu5vSliUZrswI7qjon5JX1xZSnKuzwi0ZjhY7JdDfssttwCQSCS4+eabk88LgkBBQQG/+93vvp3WHaJouonkExl0fi8kWWTeuUfx6Hs7uOvUIJUtk3HdW09RNHpK0kERPEXI/acgWAba6tsR3Lmox0xB8BakrdOIpDin4YLk7JAHeiL0nkztW8+Rc8a0/6r9pmkhmZaTM26SORnPsF+kq0duhusw/blUnj6FstIjSHzRcYe70B9g8crXmHDCKKyGJpSC7qiBw0msuC45+QqKJ20/ELO64R7xDAgSlhFF7nUBxo43QFAwtYaUvEcjVIFg68yb/Asmzf1jMo9s4Y2XkSf6WfnMRtxZCiOvG4Rt4+TsSgI6Frm5PmpqwmTI0BmohkpEhoAqYlrOCFtRF6JbUQ5uOUHEkrBtG3+siq89PbEtk4q4QqmcC4Cs1aFmFQDbiVR/Qa4AqphxyL9rXNj85qTuTH55A+Xds/nFsV2oi2gAFHkFEnttWEbXLyd2yjXURNxcPf9F/nTzrygcOA3BLaWvz6x4QABTj0OsAcHXBbX/VWjrUnVmjHWzKThrJjWvPEDeGb1oeG0mOSeciZKdS/GY/wPkNjV1JeCIym1dinL01QB4pCjair1S6v5LfZoMhzbp8sJbbcJsrE9Gx7WGsNt5BbiGzAQEwELb8kJLuTSnpJmanYu2cipq+Y1pbd3Wm7C+fqPlGYOYHcBffifaqltT1sxVLz+U/JwRqsA2dd6Z92nyRPvGK8bTLEQINdTy0HUXcPX9f9grhzzEvHOPoosqUzZ5EIhOpGur050RaD10mTt3Lg0NDcyYMQOAd955h9/85je89tpr9OrVC4AJEyYwcuRINE3j+eefRxAENE1j2LBhTJv23/lSB8p+OeSthdWnTp3Kfffd96026PtCshNrJvleixt+3ANBiCYdlsTOdVS9/BD5o65Gze+CVrmNqpcfomTMDAR3LspR49E+vhdl4JUttZXbBqzWWsvq4NuxWspCaVU7MHWL4IgJRL+h5EmGDN8Grblle0/M6+oqufrl+bwx8T48aXa4G+JxFo29CwsJW/RgJmwwalLe11pzvEM/iFSDaaWe9JTPQKvfCJoLM9YmUCgHS9haV8H//XUh03/7a/J9uZQFi9j0px18tnYTAHk9smiujaeEqf9oQn/snEx/ytB5uC03EVGgiyJg2070WW1jmAE9uwAQsSRMrRpRjxDKLoBoAxUx1RF0M2OIZhTJlQ1ArP4rwHHIYxmH/DtFNKDyla+Ye/Jh+Et9mE0aD43shS2LZLvi1O+1YSkHS1Cz3NiJEHOvH012wEft649ScM7Efc77CD4kycLcPJf4Z+1OH7VQS7m0llBeLUJw5HiaVv6J/JEXYqyb3XayOegW5L7j0NtXbymfQcTwAxaibWCkSanLKKv/cNmXTUjoVOyVqtbwjwUUnvVbEu2cZ3XQNOzDz0fb8kJLaqXjmO9rbhckN2KwL3a8HsuSME1oohjf0IcQMBAQqFl6H4md65Kfk4MlNFZpybSzSH2cFY9v4NjzerHzpXqOu/wInr/jCppiMbwuNz5fgN91K0HUdGJmi21nnO7vDSeeeCJ33HFH8vGqVasYOHAgq1atolevXpimyZo1a7jyyiu54YYbeOmll8jKysI0TbZs2XLQ23tAycYZZ/ybkSQBXYoTFZrRpTiKKmJYKrFmk+qQTO75M5NiGGa4DlxBqpc9yJ5FU5D8eSC6HGGXj+91diA3/QFXlxsQFCcsqLV+qeDthr7rHYhVI/efQu0bC1AKehL1lGZyuzJ8J7SvR1429VUKxy+kwRtkxiuPMOdX03jkX39BPP72FPE2efAdlBb0ptRbQgI3gjcbBAGzqT5F8MjpB9NS+0GXadimnXTGoVVpeDpKzhHgLya85jWgRXjm4tmo7nw+3rmBcU9PY8brc/Eis2dtXfI+R/2ke9IZB2dCf++xdcSbtYPyHWb4YeCx3MREEV9LDfKG5gimZVGS7UzJmuWirtlZaDaohajxGqriKgklF7dRBzaYliNWpId3A6AKEDdJSSnL8O3Tfs63LTspDJltwvuzPubLZV9hRnQadA8FF6WKYRVcPItb33iKCS/cRlleDpItkDfqWvS6BtTSWzqMd0g5CK5ctI9mpI55n8xC7nVBsk2CpwjRm4vgzcF3xMAOqUL6J3dhNYdRBt+De9jTKIPvwRDzUMwIWUJTsvJFe5yUuoyy+g+V1rzw9rRqFu2dqpZzwpnJk2xotdGZCEYEu/YT5KOnAhKCp8iZ27vfvpet34D2+QLk3pcgHH07K5/bg2wLGKZN1PRSp2VT0eQhe9j4lP5UNGYOn7zuqK3n9fAzfEIPTr20hPwuFu4smTVPfEmooZ7LF0+lOlJBJF6DYTYSJ5oifpjh4GLZNrXNGnsa49Q2a1idNIcNGjSIXbt2UVtbC8Dq1au58sor+fDDDwHYsGEDfr+f6upq/H4/Xq8jTChJEn369OmUNhwI3zi6nnrqqU5NzG/g3Xff7Yz2HNJIksCeaAX3v/EUYwf/nK55JVTKVrKud2m2i4fO6kPOOQ/jD8o01ep8+k4zPY65ioLhk1FopnrpLIpGT2obyEJfoH22EPXIqxCzegACidV3QzyEOvh29MZmal9/yAkNFpSMM57hO2XveuS2FKc6XM/M1x/nqhEXslNT6TLkfmQsTEEiJnghKmBi4yFK1ZPXUnLZAzR8+GfyR7aldBAPYRPE7jaPprhGoV9FW3UH6lET0oojIkhsfD9KyTFXkXfSROIRm9Wvhek/2pm8j+1+FDefMYFNNdtShNy8uSrHntcL1SejRQw2vLmDuu1NWEamX2XoHGzLRrE9JAQpWYO8ptFJh8j3O3ZmWS4aI18AAmagEDnaQGW8J5qUhT++AwDTVpx/4zXYto0qCpi2k2KUcZsODq1z/thFt7CrvpJl4+cnxxNRERlx3SBCXpnJyxyR1OG9gtw5fiGKbZCwLL6MhfnFiT/j2lGXkB8LJ0tEysESii57EKXbA0iyCYiYhuikscbq0gvCqjlAy0bn0VPRTRnbNpGyctOebILJjnvPAxxnpuSKR6l+/LcYoQq8/U6l8MwZaKunt5VlHXwnYc1DJvruh0lE8xAYfGdKbXFl8J3oZsdUtX3ZnOAKog65H622kvD7iwgObZnjNQE17yoEVxZ2ognts4WOWGvf37L893uo2x7mmJ/3QpJEqmImk5a29acZ455AiMVRfR6am13Em2rJ6+FnyDk+QkuvJdLSn4acey8bPoQjCrrz998u4qu6r7n7z49zfvlpTF/2MIvG3kWptySzhj7IWLbNlzURrn1pfdJPeuC8fhxR4PuvS3i63W4GDBjAqlWrOOWUU4jFYpx88sncfffdgHNiXl5ezkknncTjjz/OsGHDKC8vp7y8nLPOOguPx/MNd+hcvnHenjVr1sFox/eCODHuf+Mppp86kU+f3kb+L2Um/2tDsg7insYEV7/6BQt/3h8N+OT19Qw8LR+3T0BRBKqfuqFFffWXKYJvdugL9M8eQRkwCVxFyEddh+T2Yekx1PxSSi69BwGBqOWDjNhEhv8hvJKXJy67k8ufvJVxi35HWW4xf574IHkuCdk2cRFFdAeIxS1EW3cEh4xmCs+ZjNFYjdB7CpJLQfLlEdFErnp5D2srmllzdW/HSdfD6cURbZWNb+1m41ttbcnrESDg01hz4+9xu/xMWfp7qhtDzBtzC+sW78SdpYIt8PFLW5Lh6idc3IdN736NKAsZMYUMnYIeM4hLzkm4p+VUpraxCQEI+p3x28ZHNPIVJqdaGAAAIABJREFULm8JhbkeqprD1CRULEDVHBEjy1awbVBkg0RjDaqvEICEJSBLB/3P+kESJ8bYRbdQGMhlxjkTcQdUhlzRl11rqpHMRgJZBtWNOvlehT2NCd7eEuKL6iiPnn8kVy++iTU71wPw9sQHMF+6Dc8Rg8kdcQmiJGFbFrGwgCugItg6TZUxtqwMc/xZuekV0l15uIY9jW0Y1L+9mOxhlxMVsshqKa/WYYyMt5UxKxozm7rX5iadquj65VQDBefOA9vCEmTCmqdF0C3DDxHDtAlLxfiGPohoG0mbsE06pKqJnpy0Nmc01iFmFeAqLME1ahxGpAmh742IajaJ1bd3eH+00aZue9gpPSaCJopMajngApL9ae7Jh5GnKyg+R1DZaKojtPTqlDD60NIbOebyJ/jb3Lb88lvG/BY1X2LGOROZ88aT3HXuZBQyGgkHk/qInnTGwfGTrn1pPYsvGUS+X/2vr19eXs6HH36Iz+fjuOOOQ5IkunfvzpYtW1i1ahU/+clP8Hq9vPDCC3z++eesWbOGF198kSVLlvDSSy+hqv99G/aXb3TIy8vLD0Y7vhfolsHVP7qYT5/eRqQ+juRTkkbWyp7GBLGYgYrNKRdkUb3E2cEr/e0TycGj6aM3yBuRujutDpqGJboJ/eMZgkNPJ/Fx+9ycG9C2LsXT5xIstQsJLeOUZ/jfIGpGmfu3p5lxzkRyvFkU+LLIF5vQ3m+zbU/5DERvF0xDofj869A/vYdEMq/xDiwhh7hhE4nFmHRikNe+9GDpNnL/KRg7Xu6gFiwfdwe64Us5+c7rEeCUiwMYK64mu+V9946awtg/Ps6k1+9i2vlXcFTxEfzroXUp4eofPvsFw689BrdfpTmRCVvP8N8TCceItDjMLtFxcOqawgSz/Lgkx0nC9mE07yQr0I+SLJEva2JIhk1z3EDWW1MsRAxTwuOTqd+5idx+jkMeM218mfDLg4JuGRQGcpl2+hVc//xMdtVXcuWwC5hywgiqnrgcI1RBVrCEub+8j1oK8Qg6tXGQdZF5p9/CpNfvwhZsuuXk03zEYPJG/BJttVNZQiwaiqfPJWgrnLHS6ynimJ/cjmFaqIOmoX0ys20NMHgGVUtnE12/3EnPOX86tuzC1GwiUk4HxWul/E4MIZgsYyYINtENy1P+tuj65ZinX0+TFWh5JuOM/9AxTHsvUT/HJlpT1SRbQ6vYQv1bT7WdfrfTLcCdgxCvIrGX7TZ98i6BwR3XvIpq0HdkF7oNLsEQbAyLtGtqJaDiyVIxRRsRi6wChfBeYfRGqALRTuDOkonUO/P7usU7Kb+qF9OXPcycX01DEDI2frDRTCvtb6p30uHiCSecwIwZMwgEAgwePBiAwYMH88EHH7BmzZqkKLkgCAwYMIABAwZw0UUXMXToULZs2UK/fv06pR37wwHlkGuaxty5cxkxYgTHHXccAO+99x7PPvvst9K4Qw1FlOmaU5pc0JsRndJsV8p7SrNdSIZJwBemeskNSSfcbA4lc2ECR5+CvukZ1ONuwT1iMa6T5oK3CDNhETj6lA75YNons5C7j0JbNR2f3HwQ/+IMGdqQJYFsT5ygu5lsTxxZEjAsg7+te49xi37Hzx++hu4BD9qq6R1yvt2EkUQT/dN79nrtNkRBo3bBOKIP/5z8Zdcw9QQ/6HFq33oOOzgS0/aiDL4H9UeP4TppLpruRWuq5ccTBzg768DxowthXWrepX/9bJ6/bDrTRlzBzH88Tl2kIdl3W4nUx7EBQcw4OBk6h2g4RnOLw+xumYFD4WbyswO4hCgAtq4ixOvxeY+gNEvCsm3MWBPNzY2IZpuNWrYLj08htPML1JZrxcyMrR4sFFFm8mmXJp1xgP932FHJ0HNwHIHIC1PpmtiO8ej55C+7hlyzgt3v1LHworu467xJaLZN3shfo7U7JWyd09uPWWxbjKpY2LIH15B7cZ38MMqASdhSwNGkablf9YszELHI9sTxiU2YSh7y0AdRhy9BGvogYauYsKbSZAVoNj1YtpBcf7QiB0syOeMZ9gvTtGk2PZiCSt1r96NVfYWpgXrifbiGPYV60gMIviKI7E5uJInBvihHXwWWhr/fiegJG2XAJFwnzXXK+G58HP3DmxjwkxwEn4RpWsiikHZN7ctS0bAQTFi7bCsCVlp71ut2MvC0/ORzkfo4sXiCXfWVXP/8TMyM/sZBR5XEtL+pIh2Qe7pPBg0axO7du3nzzTeTB8zHH388S5YsISsri65du7J161Y2b96c/My2bdvQdZ3i4uJOacP+ckB/8d13383mzZuZPXt2Mq+8V69e/OEPf/hWGneo4caDpIhJJ2D7X3cwc1TvpLGVZruYOao3fiOMFalLyblpWP40BefdihwsQfJngxYCSyOxYgrxty5Ee/86FI+EFCxOnz+mBJx/7UxcbYaDT2uNUnPFNWhvX4S54hoCQiVuRaEst21Qk1qUVdtjx6rAtlAEO+1rZru+YoQqIFJHeO0/CA6/nOqX5/D1nIup+fNDCKJN4v3JCB+OwbVpKn53BaOmHM6ZM47EVyilvbYV1qh7Mc6DZ9xKfnYw2XdbaQ2Vy5Chs4g0xYm02JRbtLFtm4ZwhKDfh0eJY9kCibpG53VPGaUeR2jGiDTS3NxA+zWjJbjxZbmo3b4BUQBZgHgmrPig4cZDz4KypDMOUJaV10HkyghVIKqe5P9rlkyh95AAoi1xxZO3sbGhFkGWU0N2W+b0VsRgX5TDzyXxwTS0f/6WxAc3ghnH2PwMVqSGnFN/jatbf0oun0/J+N8j2Q1t4/H7VyEYjYT1AI0xd4fQ89YKGe0FsgrHzCaGt9O/swzfX2J4KRr7IEXnXou9eTaJd36NtnIatt6EYGkIsrvNGe87Dv3z+STeuwZ99U0obhFj8zMk3p+Mtuo2rNDGpNZBawky1bKYe+5RKWvqeecehVtqqQ1uQe8hAer/Oo+ii+9LFVA871ZC/3gCt69tw9KX68bv9nJct37sqq/EsjLRpQebXJ/CA+f1S/lNHzivH7k+pVOu73K5GDhwIABFRY4o4dFHH01VVVXSQY/H48yYMYNRo0Zx9tlnc9NNNzFr1izy8vI6pQ37ywFtf7711lu8+eabeL1eRNFZURQVFVFVVfUNn/xhYJo2kmpx0hX9WPfX7RSfVIrXLbPowoFY2MjAxuc3021kVvJEvHXiTuxcR2TTB5Rc/giiAmLvSzqqR6++HdfQ+zHT5Oa05tIiiEiSkBGmyHBQ2VeN0uyh85jzq2nJEyQLMW1uGYKIpWtpXzOb6lPuZRk63p7HEP7sTUouewBECcnjQlsxucPpOt0nUP/yHIp/Mz/ttWNhi0h9nLXPbGfklGP50YT+vPfYupSSZ0YmjC1DJxJrTrSckNuook19LI5umgS8HjxKnITpIVFfA4Db0wWXHEeV6zGjjTQ2N0Nu27UsS8Wf46N+xxcAKKKjtJ7h4GCaNm7JRVluMbvqKzmuWz+yPbkYacqbmdGm5GMjVEFBiYuELLKrvpK7XlvAsvG3p2rH7KWPIfe6oOOa4JNZKAMmoTfWI+cUkffTa6j+43QKR1+PueOx1PF41b7riLevkNGaHxzDm1lHZDggTNNG9PoxVtzcwfZcP5qH3VKmN60tr7oNZcAktA9vSl7PUXGX2l3fotgjseiigRiWjSwKeLDQtZZBTwRPQKR2w3KyTvoV+edMQ1Q9mNEm6v/2CGa4Di3hOOStGjEbX/maeaffwl3/ehRZlDNaMQcZURA4osDH4ksGoZsWiiSS61P+a0G39ixevDjlsaIofPrpp8nH/fr1Y8mSJZ12v/+UAzr7URQF00y11vr6enJycjq1UYcyiqmyY00VRWf1ZPK/vuLMpz9h7HOfEY7qfPXnbfQd3g0tIRBe81ryRBycCTv7xJ9T8cSVVD57K6KvJO2Jnm2bqOV3pJSOUstvByUb5cR7qF/+Ah6iB/vPzvADR7SNtPYqYrLon39ixjkT+dPEB9kejqGWz9jLfmegNTTQ8P7LqMdN3+u1O9BiGkVjZlE64TFKrvg9roISpKwsgkPPRrBjmPXbsKKhtPeX/NkYoQrqX30Aca9r2/2m88lrTjmMSH0cU7cQfBLDJh/DGTNOZNjkY5KhchkydBbxZo2IBG7JAtsmFHbSjPweN145jma50eodW3a7uyAmPJQGPdixJhoiqSkVpq3iDajUf+2E26kZh/yg48bDorF3UZZbzLQRV/Dx0hqC596betp8/nQalj+d/IwcLGFro05VAn477EI+3rGBR9//W3JsFIN9ERQ/rqGzUE+4x3ms5qQfY/1lCP5iRJef6j9OxwhVIPmz04+H6Pv8O1rDjlvD2A/UGZckAb8UI0uOki1H0EMV+KVYppzU9xBJEgioGtlqjBwpTLbYSEB2fut9rQUsZPAUog6ahuDO36ctJ+3dU4R6/O0YttruviKVMZOxSz7jjN+vZuySz6iMmUgt4c22BK4sD3KwhNDf5iPIKtV/vJ2qxTdghusouHgWvrIi/t/1x3Lseb347NWv2L22jnWLdzLr7Km421WIyXDwEAWBfL9KSbabfL/aqc74ocQBnZCPGjWKadOmcdNNzg5WdXU1d999N6effvq30rhDEdu08fUKMnnZxhTVwMnLNjL35MP4dNlWBp17GMGREwi99Rh5Z1yH5A+CL0g4EcMIVThK63WVyd1xMdgXudcFCGoOgihjSbmo5XciyE4JEitWg7H1TyhHXkJ82ycETjj/u/0SMvzgaK1R2iFyA4XrR13K2EW/Y1d9JWW5xbw68QEKT5oLtlPOR0sYYIfxHjmEpo2fknXibMxIHWZTPdWv/p7g8MsJf/YmdqyRvNMuwzbjSLKVFD8SPEWIJTMdpWF3rtNXlAC2EceIO850dP1ygmfdgDh0HjI2DRUJPlpSTd12p9xUa2i6aVptG+QZPzzDt4AW1QlLFj4FTMsmFI4A4HG58CpxNMtDIvQVusuNJSvICTddioLs3FhFXSTV2zYtBY9HJNZQSyJcj6rk0miBjY3AD3NRc7AxTZtSbwnLJs7HZ3p4fe0qYk1+Bv70AbJ9AoYhIgTsZI63HCzBdf693PevemqjVSy64BL+/Nnb3POXhYTjEW4Y8QCi3pgiwqaW3wGiJ310kW0iqWDLSjtNmsb077XsDhF0siTgU2PJk/HIf6CmLkkCnthuGt5aQPZJv6TmpTuTituFY2YT83TJnLZ/T5AkAa9RA5Eophaj+sUZbaX6xszBcuWjnnCPE56uhzG2/AE7Xo9g6TRvXouv/8mIZnwftmxgbP8zytHXYKtB6v76e7KHXQ44woKaKCbLCEPL2nrpBp68cACS6eSQb/hnA30vmk31kinU/+0R8s+ZhpLfjeYGiEvZCAmDv8/5OOVvitTHUVBImJndzAzfHQfkkE+ePJk5c+Zw1llnEYvFOO200zjvvPO46qqrvq32HXqIIAfUtKqBkk+hbnsTb93/KT/73WCCZ07FsjR2NNQSaqijb05+Moy99vVHKLloBvoXz6Acfm6KirRafju26EJbMSVFaV3f9Az5o8YnhVgkScBDFNHSsUQlE4KW4VtjXzVKw5qHbtle/njlXGqa6jFMHY9eQ+Kje9ps97jpmN4sqp6dRsllD7Drkd+khHtqezZT+tuFiHYD2gdTUY6+Cu3z+akhcRsWoJx4D0KiIUV9WB44DVe3/pjhOmxLJBzzOLvpbi/xpt0AmdD0DAcVPWoQFm38ioBl2TREIkiiiKrIeJU4upWNHqrBysqiQWggP+GmrGsZ1rrdVO41r5i2iupydo5Cu7bgOvwEABKmgDtT+uygYZo2Cm4E0dGQqdvezNuPOZEPvlw3Qy7tQ8H4hdQ0RaiNw7x/hVhb4bxuWDb3nHcdPQvKqGqsZUv1TnpumdMxnPek+zuqqx97I9r6BSh9xoAi4e13KtH1y6l76ymKRqeqXMv9p1C97H5yzphGc8tJYKv2h77iVoyW9wUG30lYKt4vp7x1jSFhUrF4CnlnXJd0xqFFYG7xFArHL0zeM8OhjYcoZr0zd9Yum5laWuwfj1F41m/Q1s5LUUu31Vxs0YP/8COxG7egbf9zh+oo6uDpaOsfRznifPRNi5F7jyP25WqyRvwmuTluWHbatbVh2U5guwVfvLWbmi+dDTG3TyAcsfELeaxeupF40y6GTz4mpQILtNOKyWzCZ/gOOSCHfOfOnfTs2ZMJEyZgmiYjR47kyCOP/LbadkhiCDb+LJXSbFfKwFGa7cKMOOFivlw3giBQFTZx5bjJyi3DaxloiouiMXOoWnw9iZ3rsOUAar8rSHxw416T8+2OsureuWRHX4Xg60aj4UWSwBPbnVIbMrNTneHbYl81Sg3TJkaMxe+/wi9P+BldfS744LpU210zA3Xo/c7ELkrpBZEkM1n+Z2+xIwCragXigKtJrLwp5drGZzPJH3U1uPOJ4gUc8RepJTQdCxCdfpsJTc9wMDCjFhFJpEBxTrAbI1FyAj4M08KrxKjTitFDNVBcRC0h8hOldO3RFfiQmoYotg2tEX2mpSKKNqpLJLTzC7r1chzyuEXGIf8OMAS7gw7FyROOZtO7X3PYmYfxm79s67AuUCWJ/qVHsGD5Hzm+Z3+O6dETe20a4dZ4HfrGx515viUCyLYtrKoVaE1bUQZMovDMCVTjRATVvvUcBWfNxI6HMRprqHr5IRI715F7+vXJ6+5L+2NfuebtaT0Vr148hcJf3O6Eynuz0o/ftvFffrMZ/lcQLR3aCRS2J+eEM5ORHdC6Np2JPPRBJNEk8dEM1GNvxKpaga6F2mxZD2MjJG1ZPWYKlhGnaMwcorYzbwNJlfW9+5AsCrhsCcGCU68cwLq/bOftx7YDznr7+F9lMfDsw9BjJrZpM/zaY/j4T1vYvbYusyGf4X+G/XLIbdvm5ptvZtmyZRQXF1NYWEhVVRXz58/n7LPP5u67706qrv/QMU0Lv1ti7uijmPzyBvY0JijNdjF39FEkPqlJCkmYhsXXDZWU+Yrw2VkoABpEPaVJYRUBAVsLp1dVl90dn1NzMEUXpmnjl2JJZxwyO9UZvn3a1yiVJAESjWBqKLLEh1s/49F3nueDKQ9SkM6esfEedSqCJCejRFzd+pM38lKkrFwEQUBw5zo6CnuJHUFLeLyZPndNzu9Owg5i6m3haJnQ9AzfFWbcJuaX8LeIyDY2RwgGfBiGgU+JsatZxow0IWf3oVaoR5D7UFrWHUEQ0JqbaDRkchTHwTFb8iv9QT912zZwRLL0GeR0jkhthgMg3WafJIscdkIJG5/fxMxRvZn2xubkumDmqN5sfH4T/U/vQVM0TL7akmiQbnzTGrBCGx2xyhZcJ80F2tYE2urpFJ47D/P06xBEkZpXZifri7u69af4snkIWPjFJjRLRsTCSJfLux8OtIdoco1hRpuSwnVyGkG7TPm07w+WqGBpMYAOv7WUlZvWngQMsEzHpi0DwVOUYsuCp8gpgdb6fk8BguAiqvlSDpBaVdYnL223tj73KFxxk3ceXpvcBDvxkr58umwr8SaNEy7uw7bVlfQ+pYwPF3+RfM/QK47iuF/0QpIlYoaR2ZDP8J2zX6PkCy+8wKpVq3jhhRcYMGBA8vm1a9dy/fXX8/zzz3PBBRd8a4085NBtKl/5irknH4bkUzAjOpWvfMXx5/cmtzTApne/ZsDow1i0+k/c0fWaFFVH07STDrNPEHG7zPTOh5Eq7iN4ihBcuUQ0J69ctPTMTnWG7wRJEohWf8nCa8+hfs8Ocku7M/PeZ5n2zmKqm8MUps01FwmOuIKGD16i6OKZhN5eSP7ICzHWzcaIVWG2hL7pGx/H2PKHjuFu5XfAvvLYBRXtAKJCJEkgTgzdMlBEOSP0kqFTiWpgCwJ+xVFab4xEKSvMRZU0JNEiEnZOf9w5uXxpVZHVJUrP0BJuHVrJ3yslarW+SYfcshyHPL9rF+p3fIEsgADELRsyOeQHHWfsiKLbBorkjB2CKbDymY1E6uPEm3QW/ro/sZiBHtbY/tJW6rc3IQL/94tzsD66HapzO4aml9+B/sXTKfdqra7S/v+O8KtFo5WFKosER45Hq9iMFMgjb9TElHzfrHNuwSzpmnbM3B8Huv0ao7Vsa+P7L1Bw3q0pOeRFY+aAJJFlNWVS5w5R2qc/2oKCVNgDIo0Unj89xaZETzC9PVkgWCZi0VBs2Zs29ULfsCD5fkSVcMLXwU5M06JQFXnywgFJlXUf8PbsT5Ih6JH6OCuf2ciwqwfSWBHls1e/ov/PevDe4+tS3rPi8Q0Mu24gDUYIEQm3dOBChhkydCb75ZC/8sor/O53v0txxgEGDBjAzTffzGOPPZZxyFuQJAHLtNmzto49a+tSXrNGW3z80haGXN6X+SuXMOm0S8iSA040jgWiArLdjGA6Od8qoG37s5Nbs3pGag65oCYHvtYJO2621Re1RCWzU53huyHRmHTGAer37OClGy/mppnPcMdfl/DSr6djfdRmz3L/KeiheqqWTMMIVWBFGyk8dwraius6hL61lkXRty7FNfQ+bD0CgoeKJdORAnkUnjED7aPpKf3CsCUkCdLpteztfHslL7vCexi76JakCN2isXeRk3P4wfwGM3yPiRrOMXZAlUkkosQ1nYDHjU91Tp2aQ47Imz8nl17yDoZ1m4Mdkig7wuTi3nv42qoiah+DKbiSJ+S5XYpZ994mBCGjtP5dIUkCe6IVHcaObq4uSUegfnsTUpPGh3M/Sflsn1OysD5ySkXZsSonNH3AJER/V0xUEoIH15GXoDVtTcnN1Tc+3qYhs3FhsoSkqoqozbsIvbWAvDOuQy0+nIonrkqJmGtadhfxM26hYPAd6KvbBORatT9aw4T3Rfs1RmLnOur/9gjBEZcj53enZMLjCNiYSKBHqHr00kzq3CFK+9SElE2WnFIkS6d0/AJs2wJRouH9F8getJd2wXHTMQ2Lutfup/Cca9FWTkNw56amXiBihTYm7doSFLxWI5bUcQPHNC0k03Jyxk2wkVLywcFxuBPNOv9a8Dm+XDeBQi+R+jh5PbI46ifdUX0yWsSgIdrEibN/meyrpd6SjF1+zxg+fDiaprF8+XIkycnjWrp0KTfddBO33norZ555JjNmzGDz5s0IgoAoitx4440MGTLkoLd1v7yzrVu3Mnjw4LSvDR48mKlTp3Zqow5l4sRQTDmtaIQoi/z4ugFEiHLpyaPJkgPojQbvPbYOd5bM0HP9VL00LTnolV35KNZXf0QPfZ6Sa4OSg/HVyyiDpiJ6CgABo7kB0W2AE/xODC+FY2Z3zCGnLR8nQ4ZvA9PUks54K/V7dtA7vysPj7kNWxQRek9BzS0E20IPVSEG2nIPmz96lfyf/HofZVG64vrRg9haA9q6x5C6/5yqpfeS2LkOgGqg8OcPgKmh1+ykYsldmOE6Ci+eDYEyNK0tLC3dAvrFK+cmHwPsqq9k7KJbeG3S7xHIxABn+O8wNZOo4DjkPkWmsc4pUel1u/ApTp3qcK1z6tk1z8fZZRU0SIfzSfBqlv/jKdxaDTcM3o0Vb2C76xRMy7HJYFEBzbXvoEeaUMSsjEP+HRAnlnbs+PvERSnrAS1idFgfeLNF7C1t450V2oj24U2Yxz7J249t57RJh1P9l8fIOWECsj8bS9cxTBfqsTdhNe9G//ot5N6XIPpK0EO7cbmChN5aQHTDcqIbllM64bG0EXNubJo1L9402h/fxN5rDDNchxgooNlyTjYLCgIY9dVUL7omkzp3CNM+NQGc37Bq8fUUjl9Io9n2G0qCgO+YUdS+tcCx06xccGdTX11HliuerDLQuunUPvXCPfwpXCfNdZxzNYeal+4mun75/m3giKRdb3tyXJx15xAEScC0TLoMyOPIH3flw2fbwtaHXN6X47r1Y83O9YxddAvLJs5H4d9rJ2Q49CgsLOS9997j1FNPBeDll1+mX79+AMybN4+ioiLmzJmDIAiEQiFisdh30s79qkNumiZ+vz/ta36/H8vK5F60olsGf/j0NX40vr+j3EiLivP4/ixa/RL/7+GxgIDL9CJaAgndZshVAzn50u7Utzjj4Ax6es3OlFybxPuT0T+fD6aB3P1sBNlLYsUNxP8xBnPt/6HQiNxS89M0bWKeLhSOX0jZ1FcpHL8wsyud4VtDkkTcfjfugAdZdZNb2j3l9dzS7kiSitcOgKAguUS0lVNJvHsZ9ua5SFIMb79TcXXrT+nY2QiymqwZ3ooT2i5hKkXErW5Ifa6iaukDSWccHDEjM2Gy65HfsnvBNSR2rnMWgc9OwW1GUq6XbgFd3VSffNzKrvpKNEPrzK8rww8ULWLQ3CK25pNEGiMtJc9UFz7Fcc7DtY1Ibg+D80IIwKddbiRRY1GaZfLk+kLu23YqshWhm/YBluXsqWcXBAEI7d6CKjgn5LadGesPJrplpB07okQZcEmP5Hrgq5V7OOmKfinrA8XrSTvexcKWc9rXFCO6fjl7Fk2hetkD2IkoghnHsiTwdkPpeTb62nnE374Ua+MsJLOB7JPaohZbc7vbIwdLiEXCWBY0xtyE4n4aY+79Lnm2P2uMTOrcoc/+/oat9hA8fQrNto+dW3fw8MRfgp7ANgyKzr0WO1qR1s4tw0JwFyAEelDzykNE1y9P3qd68RQ8OGOjLAlke+IE3c1ke+LIkpAUUmzfn068pC/vL1zPP+Z+QjysYUgGA0cfnnTGwTlF/+CJjUwbcQXg9FXDytjld4FlWVSHwuyqDlEdCne6Tzl69GiWLl0KwNdff000GqV3794AVFZWUlRUlNRBCwaDlJaWdur995f9OiE3DIOVK1fuc4I3M7X7kiiizIndjmHdX7Zx7Hm9kqEx6/6yjZN/fDz3vvE4hmXglkQMVeFzyyDaYDIiz0gOem1iVnlIQ2ejrXsUq2pFS2mI29HDYWSvB2317R1Ko7QZfow/AAAgAElEQVRXR22fj+6QWaBl6HwkSURwu1ixO0ZUtyn2BRj3wCssvPbsZA75uAeWgSsbTBtRsjA+v6+D7RaeMwsrUoPx+X1oVR3zKOWjp2LYKqJo4s5SEAQRKZCX0hY5WAK2mXYBwV4LiHQL6NrmEGW5xSnPl+UWo8pqitZDhgz/CVrUINLikHskJ38cwONSkyHrDZX1FHQp5DBfHX8J5aB4CjB219Ml2/ncu7uyubxvHwoT68kztgEQyHU2zEM7vyCv+3GYtmOumQSlg4ciymnHDgOLa167k2nnX8GR3hJqoyGe2/gqY687j1goQTys88lr1QwYPh1hfVsqj91vOp8sqQUgFraQgyVIgTyKRl+d1NYQPEW4hs4msTJ1LWB8fh/qCfcl29Gw/GkKz59Ow/t/IOeEMx2hTHcO4aiZHJf/E75pjZFJnTv0OZDf0DRtolKABArvvriIc665lWCXEiRJQt/wGGihDvovcv8p1Pz5IZSfTkOx9aQz3kqr87/PEn12MbQIKQoWNFVF+XTZVuq2OxFH7z22jhHXDcLATBvafqTX2agqyy1GFuXMPH+QsSyLL3ZWMvbuReyqDlFWGGTRzWPp060YUdyvM+NvpLy8nOeee47GxkZefvllzjnnHNavXw/AJZdcwjXXXMNrr73GoEGDGD58+HcSrg77OV/n5eVx88037/P13NzcTmvQoY4bD73ye/DXtavZvVcO+cDR5YzqfzKyKKN41KQDAxBH7jDh6l9UYbTUZ7SPHAPxGvRNi1H6XYUg2OlDev9Hdp4zNdB/OOxty5URC3yHc83iDzC1BKKkgCs7+fuLtpFeiVUUk456+zxKwVOMXrsbgzyEaDXGZ7cnJ/PCM2egnXwx2BaWFkPK7QKimnYBIYgikiAk25FuAf3HVW+waOxdHfJA8/051CVST9gzZDhQ9KhOsyQg2DqSAI3NUVyKgiAI+JRWh7yOk8tLEAXYqBzPAMD6OoG3Rw4ej05dY5SolE+zXEIwvgnbFvD5VURJoW7bekqcqDzipoA/4/ccNNx40o4dLkmlqrmOXzw5CYDjuvXjwTNuJRZK8Pc5Hyc/31gRYNAZd5PXzUNDRYKPllRTt91JX9j8QZjyMXMgXpvMz4WW8N9EKP1aQBKT46AZrkMIFjs1olfdlnRossrvJLzXYVRnzt2Z1LlDn/35DVNE3yQ3vpKuXDbjPsfW/tkqyuroHOgbFyZTMFFzqV42D//w8axp9nCUz96n8/9NJfpMwIXE8kfWprQ/Uh9PbnylC22vjYaSfdWNBzNjlweV2sZI0hkH2FUdYuzdi3h15rUUBgOdcg9BEPjpT3/K66+/zuuvv87zzz+fdMiHDBnCO++8w4cffsiaNWuYNGkS48aNY/z48Z1y7wNhv6brt99++9tuB6+88gpPPPEEW7du5eabb+biiy9OvhaLxbjppptYv349kiQxbdo0hg0b9q236T/BNG3ccvoc8khNnFlnT0VAAkFIOjAAGyJejr94NnKirsOEq62egXL0VRhb/oDc6wIEwcIWZMSioVhVK5LX2F911P+EA5mk04mAZIRcvsfsZcvgOOX9CwswwjFnw7nd727tQw3dssUOC0tsHUGSEYI90CIWysa9okJWT0c4/Gp2L7gmKTaT8PgovHg21c+22V/BebdS9+osckaOT9phugX09aMuoyxQyrKJ8zEsA7lFZb2zdmoz/LBpDVkX7RimFaAxEnVKnlk2XiWGbinEaus48rDDaTADZOUfS1OkHiWcQA8VkRuoorKxGcuWqVd64jVqwDKQ5ThZJd2p2vQxx7aYaty08csZpfWDhWnalHpLOowdmKSMM7eOupK1z2zn2PN6pawT6raH+eA5nZFTjgW3RrxpN+CsHfqd3pOEX8Kf7Sexbi/nW2tIO56aoocuE59AsA1sQQZBQnv/hlSHZlVqzfH9nbv3dz3QPqy9NUc9szl/aPHvfkNJEvAKUUQzjl67k6bP3ybr2J8BGtrWh/YSZZ2Fa8i92IkGbD2M/uWLKP0nknv2TXxQ7yIUt9mAl2Mvmk1oSUfnP8sOf3OJvn3kkxuaxYY3d3DCxX1Scsh/NKE/RkBj2cT5jjOescuDjqYbSWe8lV3VIXSjcw8XR48ezfnnn8/gwYMJBoMpr/n9fkaMGMGIESPo378/jz766P+uQ34w6Nu3L3PnzmXBggUdXlu4cCF+v5+///3vbN++nYsuuog333wTn8/3HbT0m7FEm5PH9+dfC9a1dfzx/dm9rpYeRcUkTBPFtvEqbY5MKG6jZ+fjCbg7TrixKgRXLkrfcXuVepqBDslw9v1VRz1QDtTBTicCkhFy+R6zly0DeBUB9pHiEtE8BAbfib761jZl3/I7sWirHCAG+3awd//QWcTT1Rn3ObG8rWIzxeMXYgS6UTrhcYzGSszmEPV/e4TEznVoFZuTdrivBbSuWSi4HQk3k8yOeYZOQ4sYRCQBkSiWZdEQiVAUzMI0LfxqjLjhJuC2KA6KbNXzKMzvzReV6znKH0av7k5xVi27q5vZHcmjqx8alTJyrK9w0UBej35s//ANJFMDVOJWxhk/2JimnXbsaD/OZNtZfFn/IRve3MGJv+7Lyqc3ptRPNuIGckBOqWVuCDaaZmF6lA7Ot7HjDZTyO9BXtVNKP3EmaPXoq9rGWNfQWWlP0mV0pJaST/szd+9LddvwlWHpbe1t/510duqcLAn41FjSQYzspxBdhv0j3fcbw4eMACbIIkiqjdq8i6p2dlAy7mEqFk6kZMwdaZ1nWwuTeH9ycv0aMQMIqkrCdBzoUNzmY3cp/cctJEsyMZGSzr+l7mMjv90hlCHYnDyhP/96bF1Kn3IFFAaefRjbVlVy7Hm9yOniA0lAx6IoJ5+amnBmnv+OUBWZssJgilNeVhhEkTvXPe3atSuTJ0/uUC3s/fffZ+DAgfj9fmzbZsOGDZSVlXXqvfeX/xmHvDXBPt1J1F//+lfuvfdeAHr06EH//v355z//yU9/+tOD2sb9RTDh8zQ55INGH4EoC2CAHtMo7+JhVUuor1cR8NoJtMptaQcdQfGR+OjOvfJupzs7jr1+heDOpdnIxtA6X2DvQB3sjJDLDws9plFe6mHVnjZbLi/1oMcSad9vmDZhqRhfO2XfhOHj64+q6DV4Btrq6ci9Lkg64+DYu9W8O33fUL1t1w5VEKuPsPypLxl1bXf2PHp56r1DFQi2TsyUkCURCQXFTF1AZ+qQZ/i20KM6YQkkMY5pWTRFYvQuKwbAr0aIJVR6HeGkgFVb+Xh9+Wyt3EjPwnrcX42ki5rLGhr5rFKi6xE6TXIXss1tuMUacnsczdb3XiH01afIXcqJmZla5P8rmKaNW/IQF2PYto0v103d9iZEWeT4X/VGdkloEYNPl20l3qQx/LpBxFsFptpN6RHNQ6D8zhRHWzjyUtZGiulR/iBZiolly9jYGCun7df4aZkmnthuYp4u+zV370t123PmQ7w1/6vkyaOd8+04OPvMJZaKM055J5D2+y2/k0i8iHce/jzp6J426fCkMw6OHZiRkPNvc2NaW7O1BqBt/eoe+iDNMVLWDwkTJF8uzfEEpmnRuoGTbiNfHnwHDZqKJDmpaKZp4fErafvU8b/qTc/BxWx692uO+cXhmJA5Ef8fID/bx6Kbx3bIIc/P7vwD11/+8pcdntu0aRP33ntvUiOte/fu3HbbbR3edzD4n3HI/x179uyhS5cuycclJSVUVlb+m0+kJy8vvVJ8Z1FQ4OQ7ROpj7F5b1yGH/Kj/1x1fvif5vnhzgqNFC/n/s3feYVJV5x//3DozOzPbKyywCNIUBRUUjRqxJhHs0Wg0NsAEJRZsidjQxAJqQKOAENQY4y9GjWjUxIZBBOzSFATp2+v0W39/zM7szu4sLLALq97P8/g8snPnzi3nvOe857zv+82WsWIGMQNCyxeRf1KqjqM6+i5sQU27um3HGpIrjt6jHybDrSB5cxC6MMRWrw+mHaRlwUzeS+v7NwJa2jwgSXVR4O+anJB0tL6WfU13/HZ3t9e27M092LbN8WVeLBtEAVySgJDhxbIsahpDaLqBqsjkZ3lbLbq13J9eFyE716ZqUVzaR/T2a9fejXVPo46+E21FSw65fPBUjHCLRIWcU0I0ZBOqi9JYpadth2u31jDuzqfTFg+xLIuvyjdy6fzfJcPYF17xB7KtA/Zr+0pHT7uerm6vPe3+uoINJoREyBA1QpEYhmmS6cvApUpkusKEAhIH9M8makoE3QcAsLFiDXXZ0C9vK33ryoBvWVcLroMUQMHChWQHGTh8OB8LIju+eIecsqMwBIGCgp4RRdYT3+Xutte9uYfWdqXIl8esS6bx5dObUN0y/33wk3bHC1b637Msi80VmQRKbyUrQ6ExrOMLZzIk30UgZFERMuN21hVCT2c/R92ZLAabKJSpVVdS/cJd9J68EHDvcuzuaD6gupodp7ooS+as4pSbDu+W925H64i+3z6XOPu4xxDcna9n1BPb5M7Ynfa6V2N5uue7YhpG//tSKpTHmiLt2oEZrEfOKaH2rYUUndVmHtucQ578neboDL/QhGCqHN8vCwsxZf7Q7trsAxCPewxdi/L1thpu++O/qWoIpIzjobpIuzxyANklseyptRx3zXCue/GPTP3JZQwpOWCvn1d305OvrSsQRZEhfYt55f7fohsGitx2nrh3dJRyndjkBbj88su75Lf2ln3mkJ911lns2LEj7WdLly5NCrZ3J7W1QSyre1bECgr8VFfHC7C4RCltHks0oOPKVKiujksouZB4b0Z8MB5x5SBue/M/TD/rl9S89Veyj5yEklOE5M3BjAQR7Fj6FUc9/pt2pBKzsZzyZ27v8nxtnySlHaQNW6Kh+Z5b378kudIWAWnSXZjNx3Q1rX9/X9P2t7vKgHZne21LVz+/AM063w2NXHnfX5Irn0/echm9srPatU2XKOHxi9SsXkx49eK49Fmb9m5H67D1MMrwyYjefsTKN1Dz1t/IHH0WEG+TOWffx5rlYU66uj/eHJG83zxB1csPJTVN3Wffwx1Pvge0FA958d7JKM2mUJeiSWcc4lIoD76+gHvO+S2GaSV3zPf3ynpXvq+e2F73Z3/uTqpqIxiigCLoNATjRQLdskJMM/EpISoa3fTvn02jlY3h6QNmjG1131Lr6cXAPl/Qu+kkRNXD1zuixJoDUGKqlwwpSp64kbz+B7Hm7Rc5dtwtBA16xDPsiW0Vdq+97uk9tETb6JQ31lDoz+WTzauZ8up0pl34GyRVTDtXQEj/7nQMfnHn3Hbhnc/ffRXn3/5E0s6+9cCVSGnsJ65cxKE3IogCVjSCaSjUvfmn+K6mFiMkZO5y7O5oPhANtTzLUF0Uy7C7pf3luGNpNydMPUZ9oHO/t6/sy/5or3t7bx09X7WNPHei8n/rdhD45FWKLrqfymdvpvKl2eSfdg1qQSnYoK+di1W/Nnms4CnCti2Mqm9oWL6I7BMnEm2et+7s6nUEzv79gpQ+0Hoc72j+rYUMQnVR6sONVAVqKW+swevKwKO6kQ11v4/p6djX4+D+cv5FUeyyAm7fZfZZpaKXXnqJ5cuXp/1vV854r1692L59e/Lf5eXlFBcXd/cl7zG21F4X8chfDmHjsh1YYqtO31yAAkDKEHlj+WomLXifLUMvpM5ViiWJce3xDyZifP0k6qg7kxqOiRVHY/1zyX+bwcZ2uo1dQaLKZkLHNLXKZnscDXQHgKipJ51xiDvAV973F6Km3u5YQ7BxZXqSbaz2rYXIB09t397XPom+8jFi5RuoemkmOSdOQioZSulNr+AZN5s1y20OGSvj3/Y7hBWXoi2bSuH4qyi99d/kT5zHhCff49N1W5K/u62qHsNsiQltK4V2WL9hXH7cOZw1+xrG3HM+Zz46mR3hciTJCQV22H1qI/G2r4oG9QmH3KUgiwYeJYYejJKd5aZJyEbIKsMObMWyTBp0HUHWcedtxuMvYFtVCLN5cm7YbmxJoMD4lJKDx1C35WvCmz4nZoHlaJHvNyRJYEe4nDMfncyYey7g1hce4uafTeCwfsP4ZMtqzpw7GUu0OOqSoe00lDuamRmmlbYAUkLDN/Hvax//N+rou9vZT/3LPyH7/ViCByMSo+aVGcS2rEpWsu7M2J1uPpB77v188WZN8hhvrjuentcNJIqCtqY7C9r+0Ojo+WqpimGs+zBA0cUzU9pBzkkTILuEoknzKbjgD4i5B1D10p8o/+vtSH3PatMeb0b/7I+weQ75J11Iw9tzOzVv7agPJMbxdLrkR/5yCGv+sxlvrpvGWICbfzaBW194iDH3XMDpj1zljOkOPYLvhAU77bTTeP755xk+fDibNm1i5cqVzJw5c39fVoeErDALPv0nk6+5CCNoEg3ofP3eVob+pC+apCFYCtBiOJbMWYUZtigtzOHTdVs4596/8dJdF5G7rlURFq0ezAiuo2eAIGEjoq+chVW/Nhm6W/nS7Ph5m3O+JElszsHZO/akUqqjge6ws4FTabMGZ5oWustP0cUPUvnMjcS2rKLx8yXkHv8w2CbYJtr6v2NH61BH341BFoUT5xMmA1OzkSQ3UqbJgUcJCKtvaadxLh09i8aIi6qG1NXm0sIcZKll9ttWCm3yiRdyw9/vT9kxv3zB73n56sdQaLNl4OCwCxqiBrhBFU0aAgmH3IVbiKc3SXoYUGjQveDrhbjtPdyiSKMed+Tl/M3k1g5na+0WNtRYDCqUsGwFBBvFrGPk6GNZ+4ab9f+aTdnk+Wi2gNuZZ+4XokSSldUhbjtu+Pv9/HXiA1z/3H1UBeqICjFcmWoy59WImbgyFTQ7/bgtS2LaAki1jcGU495Yvhr9qp+hjPojCAaipGDrIeR+p6F/9RTysF9T+fSNaWWsdjV2t50P2KJMNOYh2rQSIJlD7vapBGPa3j/INqQtCtpNBW1/iHRUdFWO5uLN3Z7MIU9U/m89LwyTgRm1SbgWkiCQfdJEGt6ei6mZqEfdhyC5sCLV6GueSO6YG6tmxFPVOlFnqKM+kBjHTdNC8kqMvX4ktmETqArzxSsbiTZpHDVhKJovxqTH7nDGdIceR49xyF999VUeeOABmpqaePvtt5k7dy4LFixg4MCBXHHFFdxyyy2cfPLJiKLI3Xffjc+3b/NrdwfDMvjzu39j+YYvuPnECeR7c5DGmBh+HbeVkazmmDAcJ1w3AkGA+bdcxhXNO4r9CjOxN8WdimTF6U/vTzGQ4vDrUQ7+DXrlJipfmk1syyogvlLZWKEhZZpIXqnLnHLHwXbYHXY1cLZGVUUULYDgySX/zJuRfLkonniESEsthbsxB19JU0xtVbwntS9lZqtoa9JLo7glD0/eclm7EHq3pCQXl9pKoZVkF6TsmEN8ADcsI14IzsFhN6jXTHALuCWLhmAIn8cNNvhd8Z0ht62j6RL1Sl8EQcJq2kSWLNPYLAEjukP09mazFYElG/W4Q27JCAJYkote4noO+NF41r3zf2QeexHRY0/E7Sj27RfaRttA3HY0hgP87vRJFGbmIlsqlirgL8lIqaiebsyWJAG/oPPOvZfy9fZa7vjbe1Q1BHjylst4+Pk3U44tLczBtsEyNSSCxFY8mJLLa0vKXkmRpcwHLJBUsV1VeEHsnpWgdEVBA06V9S6jo+drq0Layv/aLhZvNF8pheOvQltxO7FWbbA1dqQSOTMXs1WUQ0fSem5J2eU4bpoWJhaSLOIvyeDoy4eh2To3/usBJvz4PGdMd+iR9BiH/PTTT+f0009P+1lGRgazZs3ax1e054iiSGluMZ9sWc3P/3ItAKW5xbx49ex2g17ccMQpyc7ixXsnIwiQq4bRm3PA5KFXYpsa6mG3YOsBjPXPoa+YFt/10zLxuPMwA/EdlkQe7YcvVxFt2sEJ141Int/BYV+yq4EzOeAKNnawgcpnpiL588g77WqINaCtmZ12pxsgyxNtJ3ljmtZOpVFM06ZXcx8zTAtZElMGcWivJSyKQsqOOcT7siiKOB3LYXewTItGywIkvLLIjmCIHL8X3bTwq/Hd8mwX1DSB2asPgm1hN20hS5Fp0FvSPApcJkpmPu+tr+GyI13xHXIgKPUhU/uag8deQfnqZXw957ccOuJDsnOd3Lz9QdtoG4jbjppgPXe8/CgvX/1Ys+2xW0xJmrXzhASVZMXQq7dQ98ZccgO1/N9vZxDLLEWyBK4ZfyKrv92RtLNP/PZiLMmPy2UT++COdnrQ6jGPEDRbp5ztnTPbeh6T7h66GsO0k9rpcRxnvCtJ/3x33k47wiOF0ZbejuDORRk+GUHxYxtR5KFXoi29AYiP0aI3j6CRgSRBhhSDYDWVz7SJ4mhOn9jVOJ4g0S51KcqZj05mW10F540+NW2/lEXZGdMd9ivO2nk3IAoCMy+4mdLceJ57aW4xMy+4GUlIv2IsSSIuUUI2RXyiixxJp/rlh5APnopYdDSCkoH+5SPxfPKVj6EMvQLBnYtI865JRg4lEx6nz82vkH/lfFYvh9pNwXhRi30wODo4pKP1wLn0id/x4r2TkwXdklq2c69A374m6YxnH/8rhIws1KL2VdbtSCWiYOMXKjCXTkF75yLMpVPwCxXIzflfIc2DMmp6Sq6aMmo6Ic2TvCYFGY+kNhdyE3CJEi4kXKLUnOZho5huPLYPURB3qy87OHSEHjYJSQKCZZAhKjQEwmT7MjBNC78r7pDnZbmoDyuQWQbhCiwzTKasJEPWAXLVRtT83lQ02azcYWJZ8XX1sKs3Aha91G8Zcc41xGq2seTxm/bHrTrQEm2TsB2nHXwsiyY9wWD/ATx0xi2dEqRLSFCZS6cQe/cS7A2zKTrrGiR/HpXPTMWj1YMJFa9VseCqS3n7vhtYcNWlVLxWhanFawiks6PYZkrOrCQJ+KQImUITPini5NM67DWJNiXZWtwZH3pFvP7LB9ehf/kIgpKBmDM0Gf0WsrMB8ES2Y5avTTrjQLvaSIlx3Ke68YkuZFNMjt/paB2t8tjbf2s3pi+4/F5H3tRhv9Njdsi/V9gCC97/J3edeTXZGZk0hJtY8P4/uffs69odKkkidsjk3Tmrkrk5p/22H+HVi6kM1FJ84Z1oK25tt8LtGnM/lqTisxswm2owg/UEPnkVccwl5I4poLE8TKxJjy+5OE65w34iMXAmcsYTq9gJLVvJn4dadABFv7wfZBUrEgDTQKuvQT3yjwiyOxkVYkfrELDRPmoveeM9ehaNEfduhTOm63s/mnRwaprHbvRlB4edoYV1giKIVgRZUGkKhcn0xuU8s1xBTEtEFm2aDDdCZn+oXAE2yZB1y7YRBYEcdy2uvEOxt67k75/GOKKfCoAtq0SEfPLNz8nrewllp/+Gbxc9ypbxl9B3+Jj9ees/SBLRNi9ePZvGcIB8M49ls9cmbc3QSS4E787rvHjVCPrSVHtnrJpB3kmT2LFgKmagBiVTJdak8/mT65LfKz0kD5caQRQEXD+aha01YKx/LllzxtZ1MoxqwnIBEHeC2lZWdwqxOuwpyQX3Z6ZSeNYNqMMmJFMuIRHxdifyUQ/RVKlhUYCmmfikSPw7P78zrbRe6xzzTo3fzbSOVvl08xruf20efzz3egYW9u3RVdYdflg4Dnk34MbDDaddlsxDbb0CZ7YJrZJtIWlQIC4XEmo0kXNKiG1ZhRWuT68/rjUhmDEktx9RzCa0ZjFZx5xP4wdPEz70Evqf1ov8HD+G4BgZh56HKImU/voxBEnCNgwsU8UONWDrUer/91fyT70Y/YtHWuU93oztysG2rfQ757QM1J0NZ0zX95bMWZWS5rE7fdnBYWfoIYOgJIAVwtAELNvG54630yxXAE2TycAgqPZGkFSspg0AZMoypm0TMk38skyepwohKnFQrxI+2ryNj7aIjM8FWQ5TTxm9zI/JVKs44CdXUr7kBRYvmM7FD/97f976DxbTtMmQvHhcbpb8afVObU06RNvASGPvZF8WGQcdj5KZhaDo/OSmA9mxLkbpEBkRE1tUsUPlxJbfm6oDveFFlAN/iR6JICoeMhQdW9eTzji07EYWTpzfpm6Mg0Pn8BCm4e25lFx8L2JOLwQrmn4ei00sMxOpeSwVLT0uwRduSiut17qSfmfG7wRta8NUBeooyszDL2aSm+nrEfKQDt3D66+/zpw5c7Btm1gsxkEHHcTMmTM544wzeP7553G73SxbtoyZM2eiaRqaplFQUMDChQsRRZGxY8fyxBNPMGjQoG6/Vsch7wba5qHKO9MutkjRS8wt81ONTPbZ9xB98TbMYGN6/XGtAX3lY6gjpqJ/PoOcMXdS/+Eb+A8/nYgio2S5EdSuKejm4NCVuFQR2a5DW3FHq4Jtd1H/6Qd4h/yI7CPHYXzRZjX9s/tRD7sZwV2Ytj9gWUiSsHur3G36HtAuzSPRl1+99gmiWmznfdnBYSdoYZ2QBJYRIBqJ/y3D7QIg0xXE1Gw0zSSaewiKbWE3fAtAlhIfpht1Hb8sk60EUC2bvLwB9G3axl/eDjJ+BLiqvyS0vQCrl0SRvZIq8Qh6//TXbHz2Dio3rKJowMH75b5/6JimjQtll7YmHQkJqnbjv2VTePpEtI9/l7ShfUbfhb7qaazKpahH/hH9y0faR9Yd/RCVL84gvHoxck4JRRfPQMzI3uVupIPD7iAKNgWnXYr++R8Qh0/GEpT07dg0uX3BS1x7/qn0ys7CQkHOKaFh8VMUnDuN6hemp1UCADo1fifYrTm5w/eGqqoq7rrrLl566SVKSkqwbZu1a+OV/f/1r38BYBgGU6ZM4emnn2bIkCEArFmzBmE/pCU6OeTdROs8VMV0d9zxW2mRA/Q9qYQH//EGNbpC3uV/xs4bhDqqvZ6osf655oE4Pz7YfnQn2WPOQPLl0BjpOskzB4euQJYEsjxRctxBfEow6YxDInztDrLHnIEZbvJPaA0AACAASURBVELKzE2f94iAturP7fR15YOnUv3yQykapp3KiWzT96D5322somnaFGbm7rovOzjshFhAJyCBZtUTjcVts0uNh5tnuYIIhklFZRC590gIlWMb8bzyLDnukCcKu4kCFFg6lXj449DNPFr8EWbURrKacG3+kkhVhOzYl7iiVZSMvQRBlFj13+f2wx07JOmkrWlLupoY6qg7kTIL0T6+s50NlfudFj9Odqe1oUagmvDqxUDc6a58ZioCdlJLOkHb3UgHh91BUkD//A/xOarix1j3NOrIG9u147pFjzDhpEO58r6/EDX1pMa9Gail7s0/k3/mzfS58UWKJ81H8OXjtRpbxvPd7FOdnpM77HMsy6KppoK6HZtpqqnAsrrGd6mpqUGWZbKz4/UJBEFg2LBhAAwePJhQKEQoFCIcDpOfn5/83rBhw1Ic8tdff53zzz+fsWPH8te//rVLri0djkO+n0lokScMiy9PZfpZI/EtupnKmWdR/eQkQroH148exvWjWSjDJ6OvnZ/MBUOIv0I7UokoS5CRS2Z+MW7JEXBw6Bm0LkykvXMRmFr6sHNZomHxU4ie7OTAnSCe9xjAqlyKrWRCv0koR85GGfVHbNlL9pHjEJutWeuCcdsePIOquVfgiWxv55S37XuJHDQnzcOhO6hv0tBFAc2sJRIxEAUBt6IgYJPpCuLCoqpGQ8wZBE0bsZubYZYct+WNRktht0I7zM+0Vyit3USdv4hNTW6+kvIIDT+FoFGEKNoM2vAnXG43RcOOYvW7L2LbTrveX+yprTFMm5BYgnLUDOTRjyAMug6tIYwoCmltqKDEK+rbeiCtDbViqTuKRn05tmVRePGMpFOecdDx9P7NEyiqSZYnmiyY6eDQeVpSy2w9gB2tQ187H2X4ZFzHPIxyyLXojUHCqxdTkOlmW1U9hmmlaNwXXPAHxMJBRJQ8rHA9lY9fmjKe27Izfn8fsCyL8vUrefiio7jr1DIevugoytev7BKnfMiQIRxyyCH8+Mc/ZsqUKSxcuJD6+vqUY7Kysvj5z3/OKaecwlVXXcXcuXMpL0+NGIpGozz//PM8/fTTzJw5k1AotNfXlg5nCXQ/01qLHAsUKUDVc7cnQ8gkfx4esxorLIOogKCgDJuIbURBzcKK1ADNYbuiQsSVQY4sOqt/Dj2GtoWJwEofdi4qmIFamj79D9lHz8CO1ceLEW1+A2XA2ehr58ePs6Fh+SLyT7oQ/aN7kiGbctHdyFIG7uaCcbvKiWzb93amAezgsLdUBTQABEIEwjGyfBmYto1XDSOJFtgCdSERJAW7cUPye5lyvCJia+mzU823Ga1/SGPxINRemUhsRRKj/HuTzc8GHkyW9glZOQH6//dmGkaO5dNnplOz+SsKyobu25t2APbO1qhmiO1zr0oJK+89cVaKDRVzhiIPugTBlY06+m6M8qWoI29G++z+lDocejTVuU7shCecIFEEmUa0ZVOT3/OPmk5AKnZ0vh06jSCIyfZprH8uWb8g/qGEkFFC05KFyDklVDRFKS3MQW6ukJ6icQ/4CHU4npterzN+f8cJ1lXx5JQzqNuxGYC6HZt5csoZXPfsMjLzi/fq3KIo8uc//5l169bx0Ucf8dZbbzF//nwWLVqUctztt9/OZZddxrJly3j//feZM2cO//znPykrKwPgpz/9KQClpaVkZmZSUVHBgAED9ura0l5vl5/RYbcxTYuYZRLDjBdxaTXw5p10KcbKBzC2vI4gii3yZ18+giCKGFv/m8zB1S0VjxndyS85OOx7RNtIcb619c+jjrozNXxt9F0YuCn59QKyDjue2NKpxJZMicv8Db4Yfetb2NG6uISZ6aPgzOsxVs1op1PuVSPJwjCt6SgnsnXfi1mmM5g7dBvVobhD7pFMGoNhcjN9GIZJlisYP8CARjMb2zaT+eMAsijilaSkQ+6Khjgi+iEfKIeyQhmFaRlkqyK9s3We/jJGWLeptQ9AUkXyhfWMML9GEgU2ffrevr5lh1bsqa1JZ89q3pibTN0Rc4aiDJ2A/uUjRN+5LG4z+5yC7c5HPfz38R3J4ZPR185DycpL7oS3zss1TZug6UGQBbQVt7dXsVAjXfswHL7nCKiH/z4elVG/Fr1iBcqQX8Vlz5ZMQVt2MznHjCPzwpnMe+sLnrzlsg6jOnc2njvj93cfQ4slnfEEdTs2Y2ixLvuNQYMGcdFFF/GXv/wFv9/PihUr2h3Tp08fzjvvPGbPns3IkSN59913k5+5XK7k/0uShGl2j2C9s0PeQ5AkETXDhRCLpVSXlHxZGJFKlJKj0+bduo55GIZcQt17z9O4+BlHssRhvyFJIopHBUEA20aPaJim1a4wkbX1DXQlE9ePHgHLxBYk9JhO/QvTKTjjt+0mhNpHd+I65mHMQZdhCRJeqwlRSB+yKdoGuqh0WKFVkkRkW3BW1B32ObVRAyTwKjZ1oRC9CnphWjZZruYKvyY0qv3xN23GMsMp382S5bgWuW1RWrEBS1KY6zmHYyI1HA4YhkRepk5As/nPRp0zB2cTszPxHODH9+EGfjp6KF99/Dajzv71vr9xh07RkW2y0tgzM1CLLuTiPu4xbFMj9sF17YtgHnE7sSVTkt8RPEUYjVXknX49ki8HyZ9PTM3BjLbYv46quu9ugTdZErCjdeS4Y1iCTKgD6UmH7yayJMQXv5ulRdu+XwELbf3f4065KwdEidiS1DZqrHyAzDEP8YdJ5yAjdThfTdf+5ZwSEGRcouSM4d9xZNVFbq9+KU55bq9+yKprJ9/qHJWVlezYsYORI0cCUFFRQV1dHaWlpcljQqEQn332GccccwyCINDU1MS2bdtSjtlXOA55D0CSRASPi1XVUYbk2PSe8jh61RZqX5uLFY0geIoQFH/6Qle2QeU/H04p1OJIljjsayRJRHC7WLo9gioKlHp0XKKNIopEzAy8o6ajN+uHC54i5NKTaIx6MUw7rj0699fknX49VqQhbTu3bBtBb8T4aBpGpDKuUd7KyRdyhqAMvQRBsZFkgaLLZ1G5YEpKhVZN8mI3dU631MGhq6nVTQTRxitJbI3G8GfE7XNm8w55Q20U8odg13/VTqkvs1mLvKBuB55YiPqyobiqvGwU4yv1hiEjijajSy1eXqdzxiCFOq03Ja61aKNOpP9HbxPY+g2WaSBKzrDf0+hIU1nNlJBUgdIpj6M1zwnMQC2FF88gbLpwu32YgfIO8sl9SRspeIqQh99E5Yt/IrZlFRB3aop+/Rd0yZO0fx1Vdd+dAm+JmiHR96c5Ye/fQxLvV18aH4vTvV8DFWXAuWif3AvuHFyjfoc6+hbsWBP6189h13+FHanEDFbjdxUTNDueqyYKvSXC1uWcEnLPvZ/3ntxEtMlwxvDvOL7cQq6c9a9k2Hpur35cOetf+HIL9/rchmEwe/Zstm/fjtvtxrIsrr322mRhNwDbtnn22WeZPn06LpcL0zQZN24cJ5988l7//u7ijMzdSGd34xSPyqaGGIfllmNsnkZMr0BQiim59G62VSv0OuIubK2+w7zbhDOewJEscdjXKB416YznWI1cfMdf2FZVT2lhDk/echl98krwHD0ruaIeaLWinghJkzIyMZvq0rZzQRDRPmrJQ49XbY3nSOLOQT10ArHt92PviPcdtd89FP16IYIR36GJkIFsCCyZswp3psph5x6I6pWJNmpk+bxEdqU/5OCwF9iWTYNpoVgabjKBKD5PQoM8iG3Bjq1B3CWlUPdVu+9nKzKuYBOFtXVEcwsJuBT6GBprFB+WLaDr8aH8J4Mt7nrb5qtam6H5eQTFEord61jnKeaQPlD7n0cp+Mm1+/LWHTpBW01ld6aKGTPIkqvQvr0NPTknmI5u5BI2XZimjWVZmIKU1mZqteXQbxKyLwvRm0fFs7cnnXGIzxM0TUPxZWEGm7WcNQ/+NounyqjpBDQP7VaJOqBtzZBk2PvRs2iMuHfxbYeeTmfebyim4nflohz2OwSPSvTbqdjNbdh16I1oX8yHaD1mUx3k5aGj4JaUtLvkrQu9ibZBY4XG0herqN0UX8jsSHscOj8Hd9h/iKJIyYHDue7ZZRhaDFl14cstRBT3PqO6d+/eLFiwIO1nX3/9dfL/H3/88Q7P8c477+z0312Jk0PeTSRXvB/+nFfvWMa7D3+OHTKRpPaPXBBggD+IsXkatl4BgK1XENt8O4W5ClHTB/4y1NFt8m5H3ohlC45kicP+RxAI6zb9fSZ2sIa/3XAW//z9hRRm+7nyvr8Q0jQaI27qoz4aI+6UnZJESJoZbqJh+SLkg6e2yS+/G5vUEHWrfi362nlIY2bCYb+PO+Ot+o62+TZExaLJ8hM0m/VGrfhE99DxB/DpC+t5++HP+Pjv69CCRtp+6eDQVehhg4AEohFCNDKAFg3ybHcTmFBRGcKdmY0Vrmj3/WxJZooQxpJkGgqLwbLoa4eICRLb9eykQ35YqYUiwnubNUCg0hyCajaRN2Ig31TUo67+F/q6xe3O77CfadZUzivzccqUARx/aQnZBTG0zbe1mRNMQ1Ti1aglSeCrLRVMnv0qwaG3pkpBDr+Jmtf+zI4FU9k6awKxii2Ygdrkz7n6HkzxZY+gChaq1phUoDBMm4BdjHT0LNSxzyIdPYuAvXs7221rhsCehb079Ew6834N06beyEJz5RLbfEdqG97+IMrQS5APnkrD8kV8+W0FZ//+MXY0NKaXJ4VkjYOYlc1bj22kdlOQvDIfYyeVcfylJbiEYLvv7s4c3GH/IooimfnF5PbqR2Z+cZc4499Ffph3vQ+Q7fhuXGLFO1QXZcmcVfHVujZEoxEUQUsarQS2XoEUrqZu/iQwDfSvnmmRjRg+GX3Di1i2kCJZ0rpQi4PDPsO2KfGJFGoV5L5xG/aCX1H89p3Mufw4CrP9GDtZlU6EpAU+eZWso8+n5q2/xXd2Rj+CctQMgkIvLFtoJ+NjR+uwDZPGYCht3xGENhNAEQ7+aRnL//pVSr/835yVafulg0NXEQvqNEkCWAFsLa497mnWIM9zNyAYUF4exKOG08qTjTWCDBJtKnr1Q2vuS2VCXL5lfbQQXY8XRMr06owotHl/i4Fp2Vgxi+2ZYykWVrM+qNOgCUReuxdj+8p9cdsOnUWE0kPyGHOml9DLV7N95hmgVe/UrkVNncv/sIA3lq/mitnvsqroRuoPexzp6FmYUnbSAZdzSgi78sk4517knBJcfQ8m77SrqXn5frY/eCYVj1+WIgtpmHaHi6edIRH23prdDXt36Ll09v0aloUimunbcEYJNW/9DXnMr7j3H0vYVlWf1CLfKc3a43llPsac6SX2+m+pnXceFWmkTXdnDu7g0BNwHPLuonnFuzWhuijpImM9VhijeiuCklriX1CKMYKNGPXlVL/8EPLg5iqVH1yHvvIx5MGXEtI8RDy96T15IaU3vULhxPlOQTeHfY4e0RiRGaX6r6nyJNrLt3PXhT9OSpqkIxGSlj3uZqT8MvLG3YhcOBjTVUyTkYWmWYQ0T7KqMDTvAh08lbpFj5DhcqftO7bdZoIg2PgLMzrdLx0cuopIQCMgg2k2osdEFElCaZYzy3IFwYTKGh3FaL877omFGR2t5y1TYr3d0o/y1Dr8hs1XkWJMU8SyBFRXjKNKbBpj8GWVhWBbbPL9BFN0M37cAF779BsEbw6Rl36PWbtln92/w84xBJvR5xZT/+ItLfazsW6nds0wLbZVxRdlPl23hbPueJYjr5lLRVAkLBdQOHE+pTe9QsHEJ7n170u5Yt67VJx4J9nnTafqH3e1k5HykFpIcE8JaR6UUdNTbLUyajohzalp832gs+83S9QwqrelbcOILmoPu4wr5r3Lp+vidiihRb4zDCGuPT7yZ4WpfSVdG96NObiDQ0/Acci7i+aVvNZ4c91pn7hkm9S+NhelcGrSeAlKMUrhVIKfv0/RxQ+S/aMLscQs5GNmtwslM00b2Z+XGp7r4LAPMU0LwdDSypMM7p3XoaSJJAn4pAheqxFBkrBtsE0TQVaTcjwQ37Ux7Mz4zvkRM6HfJCpfmk149WICjRqhvFtT+o7a7x5C0dQJgmlaCLLQ6X7p4NBVVNZEMQWBqFlFKKyTlxWPGhGw8KhRTA1CUh52/frUL9o2ZVWbMCWZRwyVOq1lB0lxBekXs/kqWoJlixiGgqrEOKQA3DIs3hw/VtSirM2/kvwcgZGHZhMd/LN4isk/rseq374vH4NDB5imhSCYKfaz7s2F7eYEre2aLImUFuaknCeh55wI8W2y/ETxcu35p1LVEOCce/9GXVOo07KQe0Ii7N193GN7HPbeXSTGG72+HJ8U6TBE+vtM4hlkCk179Aw6m9Yg2Ubaea2r33TqIx6unPVy0hkHUrTIO8bG64uSXSzvug3vxhzcwaEn4DTNbiKxkpcwCImqqYbQxmhJAiI2ZqCWyucfBfUq3AMWImRMof69f+M/8mwEOe7M6OXrMEMBLFMA3UTchSHdW8Pr4LA7WIKctp6BKKtpF4kkScAT2U7V3Cuofv732DXfUjnnCrY9eAbbH7uUjMg2stwaWXKITDGAiIXgL0arb6D2rYXEtqxCzilhW3Ujty74BKvvI7gGP4dcNptALP0EUMfqVL90cOhKttXFd25MsZ76QJj8Zofcp4YRBGhqtMjIzsEwUkM2ixsqyNAiVPTqRyMCDXpL6SJJDVAWtQnbKlu0XHRdQlWiqBKMLLD5cHs8bF2NVFGefQJ10TxO+HE/GqvX4jruKtBjhP7vOqzG9rvyDvsWSRIQsFLsZ2zLKmr+/Rxy74dxHfgccv9Uu+aWFBb87vKkU54ooNl28dM0bXplZ/HivZNZ+sTvyM/N2WndmdbzBr+q4Zc7nkN0NMcwTBvBnbvHYe9dQdtrU1UxOd5suW8cVWnCnL/vtB5ztz14RvIZqKq4W3NFGzA1GzNmYWo2dvO5/apGlhQkS2xEEKWUea1S9DBCxhR0IxcRiSdvuWyXbTfdtVfOuQKtfP0uayd1dg7u4NBTcJJ6ugnTtJC8EidcN2KnFR49hKl99WEKzp1G9QvTKZ83lYxhx5Nz4pX4Dh8HWpSal+9vkW867w5026bmhbsp+uUD4O+LprWPwUka3lZSEY4+uUN3kk6epPDiGYTtDNJV6PUQTh6bd/r17cIo69+aR87YK6h89ubk+QrOnUbgk1fJPfU3NH7wPNknTUR1FXL3lQegaQqRSOJ30rfxzvZLB4euZEeTBoBfhh3BEMMP6ANAdrMGeVV5GI/fk5I/7omFKakrJ5xTgO5yIwB1RssOkOQKUhaNH78mUsIYbQ1+dxhBlDii2OTDcoGV1RaD/dXx3/CeiFX1FIPz16B5T8F13FVEFz9O+P+uI+P8PyFm7r3MjMOe0XYekLB3OSdORLOz0KIJG9XSPkzTZkjfYl68dzKGaSFL4k4rVSvIKBJEcaW10xEykCSS8wbJn0feaVcn7XLbOURPnmOku7aSCY9T/szUdmHOPySJ2NZjLrQ8g5IJj1M+79edeo/pnm3RxTPBk4FdU0F5c3vJGHY8Rb98kMq/3kj5vKkt84FmhYDEItGu2m66a29Y/FS7vtJSOyl+Dmesd/iu4Tjk3YhpWi1SDB3YANHSCa9ZjBmsJe/06+PST+EmhMxCFCPGjrmTUoxnwwfPUXj2VEouvhsrWo/Hn49GeymRjgzvD2nwcdi3tJUnSciNtR5kZUnAq0YQbQNBEJD8eUnJs7YhaP7DT0864xBvw9UvTCfv9OupfmE6JZPmEbR9yKad/P3OXeeu+6WDw66wbRvN1HHJ6i6PrQzrCLaNl0TxtbgN7pVRBcDWDTVkZPRLHi9YFv0rv8VSFBqKSrC1GFmyRL3e4pCLchQfOkWWzppoCbquIEsBZEXgkAJwSbBkq87wkrhDHvH24b/vN3LWT1S8dX8nmH85ruMmElv8BOH/u56MCx5B9OV32fNx6DwdzgO8OUT0dIJOcQQB8j1G0t6GtI53GBPszE77pMhOF0lbzyF68hwj3bWZgZpuDdX/LpCQGG1N22ezq/eY7tlWPnMDJZfPSjrjAOE1i1Hy+1J69TywTQRRIWB4MZs3kFovEiX+3dlrj21ZRd2bfybv9OtRSw7EFNR2c434OZ2x/ofO2LFjUVUVlyuuanLkkUcyduxYZs6ciaZpaJpGQUEBCxcuRBRFJkyYwLRp0+jbty9fffUV9957L01NTei6TmZmJo8++ij5+d0zTjoO+X4mIfkU27KKymduBJorpU+cj2BbKcbT1fdg8k+6EG3p9UmNUCnvbmSppN15OzK8P6TBx2HfY5p2m0E81Rn3CxXoS6dhNLfforNvovLFP2GGm5BzSlLarOTLSduGE867bVmYlhPt4bDvsWyLa56/i8+3reGJX9zD8N6Dd3p8tWbgEjQUwweE8brji6i9vXFnedM3DfiHD08e36d2K249St3AYYS1+O56tizSYLQ4Z4IAkhqkv6HycbSQUDQ+4cjIMNFjcGiBzdKtBr8+LIocayDiKcKTVcKzz33G5ZfJeOv/QSj3F7iOnUjs/Wan/PxHEL25XfmoHDrBzuYBHSFLAnbTt5jLb0vaU/+o6QSkXedrd2SnW88b0i2Stp5D9OQ5RrprM4P17caYH5pEbKKdtX0GZrA+5bidvceO3juC2G6+mjXyR2hLr0vOV72jpmN1on125tpjW1ZR++pD8YUD00NHUXEODrNmzWLQoEEAGIbB0UcfzdNPP82QIUMAWLNmDYIQT9OYN29e8ns33HADU6dO5YQTTgBg06ZNeDzdt9jo5JDvZxJhvulkyyxRTcmTyTvpUoxVM5IakHakEm3F7XjVSLvzJoxXa35og49Dz8KrRtA/mpbSfo2VD5B/2kQaFj9F4Xl3pPQDyZs+1zHhvDtt2WF/8b9vPmbJho8JxsL8+f2/7vRY27KpsW0UI4SZkDxrXq0v8TdhW7B5UxOeLB8A2cF6CppqCBaV0hQ/Q/zvbXbIoTlsPWJjILEhFK967HbH89CPKLZpiMGaGgt3uJxwRi+K+vTlmw31bGkoRY6sJ6NhEVJeP1zHTsBqqiT8jxuwwo1d9JQcOsvO5gEd4VUjxJbflmJP9Y+mpZ0PdJbW84aEnW1Na7vbk+cY6a4t8MmrFP3AJWLTtbOiZsnR1uzsPXb03rGtXc5X96Z97kkfcfhuYFsWRqAWvb4cI1CLbXVfOEMoFCIcDqfscg8bNizpkI8dO5Z169YBUFFRQVFRi8RfWVkZXq+3265t/1vOHzg7Cx8LSxkUXTyTymduiO8MZuZiNBu3BHakMu1KZkf5vK1zbBwc9iWibaRtv2pRGQUX/AFbdlE8aT6CGUOv2Uz9e3EnvXUOY8G502j84HmnLTvsVxavW4bPlcGxA0fxn7VLCMXCeF3pJ4axgE6DBLLeSDQMWd6MZK54liuAZQiYpo0rMwslFqKsahMxr5/6nBzM5t1xgBxZ4puIlnJu2RWgV20xUpbJJw1lnMTnuD0xQObQAlDEeNj6hUO3EcwZSm7ZUERR5MuVNRSfdhiu0Ke4lCJiBWNwHXMlsSXziLwwlYwLZiGoTmrTvqIz6T5t6cie7s0Odet5Q2KRtF0OebPd7clzjHTXln3SRGK+UgonzkcWTAxb2uUz/r6Rrp3FJC/ZJ01EK1/XqfeY7tkWXTwTS1ZS2svuzFf39Np/aO/v+4htWWiV31Dx1A3J9lT8q5moRQMRxK7ZM54yZUoyZH3q1Kn8/Oc/55RTTmH06NEcdthhjBs3jpKS9pHGV111FRdddBEjR45kxIgR/OxnP2PAgAFdck3pcBzyHkBH4WOmaRP29EoaIMklYXqKkiuOENeATLeS6Rgvh56GJcgIadqviUKT5YZmX0OSMsjs5UEp6I8gK/SaNA/bMkGUsAWR7HE3O23ZYb+yfNMXHFo6lGElB/L66sV8unUVxw4cnfbYmqowEUnAbdYQDenkZ/vRDBOPW8UlhYiEBVSvFwWbARUbsGSZuj790bRYynlyFJmgaRGzLFzNExVJDaCYCv2VGj4NlmGaEm53BPDiluNh6x9sNbi0aRsAWlZ/8ktK2PrNenT3z5GMelwN/8FQS6HoQFxHX0ZsyTyibz2E+ye/S+4aOHQ/O0v3SUdH9nRnO9SSJOAhjGjpWKLSzo62nTfYsouiSfMRrPZziJ48x+jw2jQLDQ8FBX4aqgPs74WD/UG7dmZamLvxHtM923DzLnVGvodeE+eCbSK65E7PV/f42n+A7+/7hhmqTzrjEE9/qHjqBnpPXojsz+uS32gdsg5w7LHHctlll7Fs2TLef/995syZwz//+U/KyspSvjdhwgTGjx/PsmXL+PDDDznnnHOYN28eo0aN6pLraosTst5DSdFnFsCWXRgGqKPvRvDEQygETxHqqLuImOl3ZlprkTr65A77m5DmQRk1PaX9KqOmE9La6oXbiBnZgI0ZqsPWwljhBrAMooLXacsO+5XtDZVsa6hgWMlABhb0Q5UU3v/mow6P37A9CECTsY2mUJS8TB+WZdOrwI0oGTQ1aGQX5lO2bS2KadDQfzAhTWt3nrzm6ke1rcLWZXc8qH0gjWzVcwlHXbhdoeTnY3rZ1EXhi03ViEaEsLc3xaV9KN+yCdM0iXrHYEteMur/BbaOVDIU5aBT0de8hbH69S55Xg7dQ0jz4Drynl3a0wQdSV61lbgyzfjuty3KiEYE0TaxZVcHRbN67hyjJ19bT2N3n1W64yVJQDQiGE2VxMrXU7/4edRRd3XYPh1ZXgcA29DS1iSwjfZjYFfSp08fzjvvPGbPns3IkSN599130x5XVFTEGWecwX333ccZZ5zBm2++2W3X5OyQ9zAkSSBDikGwmspnbkyRO6t541Ekfx4F4x9AEG1sC7T6Wly5UaD78hocHLoCw7QJSMV4j56VXFkPaJ6UAi+J9q9Xbqfuu0mw8AAAIABJREFUrXlkHXM+la2kTYoungG+UlQz1OEuj4NDd7J80+cADMjviyLJDCrqz4pNX3R4/LfVcQ3ymF6JbpSQ5Y0voB6QHQAJanYEOHlAHp5YhPoBgwmYBul2fvKV+HBdoxn0csVz0WV3AwD9jSgAO0I5DMyvRBBFbMtiZCHkuGDRuhhXH7OdUEZvivv24cvly6jctpleZQOIeY/C0/Rf3OGPiXrHIA85GbN6I5H//gmxeBhSflmXPDeHvSdlhxsFfP2QdmJPW9OZqugt848aKluHop93Bxn+PMJygWNrHdohSQKu4LakrJycU0LRRfeh6wrq0Q+T0B3TTQXbdGR5HVoQZDVtoUGhE+ole0IoFOKzzz7jmGOOQRAEmpqa2LZtG6Wlpe2OfeuttzjhhBOQJIlYLMbGjRs58cQTu+W6wNkh71EkjJRZvjbpjEPzwPmPu8g+/leEVy9m+xO/IbpjC5v/eDZVz98OwepuLYLg4NBVGKZNY8RNfdRHY8TdzhlPtP+KZ27Ef/jpSZ1RSMirTMVj1O9yl8fBobtYsekL8rzZFPrjRWGGFg9gU+12Kptq0h6/uSmGZNu47bgkld/jxuV200feAAJkaiK5ikDDAYNoEAQsO70tT+yQV7eWPlMiCKJGUVTCI8RY09gbSTRwN2+SSiKc3M/is0qLbRvWoLtyKCiLh+5t/SZeuMZUe2OofVAb30OwwgiiiOvIXyLICtH/zsDu4Hoc9i3pdrj1qo0ENU9ae9qWXVVFT51/TG03/zDrtuMh3H036PCdxUO4XZupfPYWjKpNbHt0AlrlDsqfuoXKxy/FE9lOhpB+cchpXz88JG8Oxb+amVKsr/hXM5G8Od3ye7Zt8+yzz3Laaacxfvx4fvGLXzBu3DhOPvnkdse+8cYb/PSnP2X8+PGcddZZDB48mIsuuqhbrgsch7xHkVjBFlVPh3JPif8XmwvuxJ2UGzFD9e3O5+DwXaJt++9IdiedZqozkDvsC2zbZsWmLxjRZ1jScR5aPBCAj7d8mfY7OzQDnxlDMeL22+vJoLCkmOzYagB8lsKWjFzqRRFrJwurObKEBFRrevJvggCypxEzksUgVznL6uMFZ3y+lnC/E/uBX4XnFq/FsmyEomFkZuckHXIALeMwsGK4Q0vj53X7UQ4dj7l9NfqXr+3uY3LoBtLtcFc8dUOnbd+uqqLvav4hqp4eIWnm0PPoaLEnMYYnNpQS47VopQ9TdtrXDw9BFFGLBtJ78kL63rKI3pMXdmlBt3feeSclf9zn8/H444/z5ptv8sorr/Daa68xefLktMfPmDEjedy///1vbrvtNhRF6ZLrSofjkPcgEkattdyIq+/BlEyYQZ+pC1DycnH1PTgp/ZRgX+RbODh0N23bf0eyO7ujmQpxvd4sb5RsX5AsbxTZ2U132EM21GyhNtTAsJKByb+V5hTjc2WwdOOn7Y7XQjpVgo1o1GNGXeT446lFff0Gmca3ALz7yRaCPu9OnXEAURDIVSRq2kifye5GjGgmg9RyPm8ow7bB52vJI3fL8PPBFl9VRVn8v/8RyDyA4j592Lbhm2S1d0vOwXAPRGlcimDGZc+kfqMQCwcRWzwHK1i7B0/LoSvZW93vtJJXl89C8ghk+4KoHgvJn9ex3JkW6RGSZg49j9aLPenmrG03lBCkHiuZ57DvEUQR2Z+HklOC7M/rMmf8u8YP8657KAmj1rD4KQrOnUbGQcdTdP7VoD2BvmMK2vZbKbpgCkUX3UfD4qdw9T2YXpfPoM+1C5BUyXE0HL7TJNp/+Kv/0fuqx3AVFtP7qsfIOOh4YM80U2VJwO+qwNh0Ddq6X2Bsuga/q8LpKw57xPLmXPEB+f2SfxMFkcFFA1ix6Yukg5tgx8YmArJAUNtCLCxQkpdDH6uckctuQWhOkVv7TR0ef+dqgOQpMjVaW4e8HkvP4ECploDhoTqchd/fkHLMsb3h8CJ46c13WV4Ovcr6Ewo0Ub19a/IYzTMCsFt2yQUB9fBzsY0Ysfce69T1OXQfndX9liWBLE+UHHeQLE/LAmTr6tilN71C0a8XIntNjG/jtjH27bUUXTCF8Ff/o/Dnd6XqPZ93B1Jub0fz2SEticWedHPWkl/dQtbxFyc3keScEixJdTTFHRza0KMc8rvuuisZ13/BBRewcuXK5Gc1NTVcfvnlnHrqqYwfP54vvui4iM53lYRRMwO11L35ZwrOvAa9aga2XgGArVegVz6AkpeD5M+j6KxrYPMcjBXXEvvgt/gFx9Fw+O4SIYOiy2eRc8w49BU3o384GX3FzRSOv4rSW/9N4cT5xHylZJ80sdMDudcdQdt8W0of0jbfhtcd2Wf35fD94cMNn1CaXUx2hj/l7weVHEhVoI51VRtT/r5mY9wxrtW+IRKxOM23lZO3LUDJ9YBko+s2isePIHbObuen2SFXvfHc9cywm0K5kY9rDsDjqkdWWoZ3QYCJh1iUZruY//wrxPodCYLAui9advVtyRvfJQ98jGAGABD9BchDTkT/6l2MrZ938ik5dAfpdriLfzUzxfbJkoBfqMBcOgXtnYswl05JmRe0ro4tKlY726hXPoD/iJOofX0WhefdSd+bXqZk4lyE/P5OQTeHDkks9hScd1O7OWtsy53knnAu4a/+lxyvw6YrZXGocOJ8p6Cbww+eHuWQH3fccSxatIhXXnmFSZMmcd111yU/mzlzJkcccQRvvvkmt99+OzfeeGO73YjvOq1XsAsu+AOCoiYNWwJbr8AWBQrOvglj1YykxqMdqUT/aBpe1XE0HL6bxOXOfBgrH0hp19qK2xFkgaDpQdOs3RrIBcFI24cEwclVc9g9wlqE5Zu+4KgDRmCYZspnI/sMQxRE3lzzv5S/r62Mh46boVqmFH7DGfr7BEoOx+rdF0NWaKiJkV2Uj9nJopx5ikyDYWJYLe1dyagDwUQP5TPCtYlXd4xAEGz699/OwIGbOOSQteTl1eOW4dqROi5V4d7/Bckt7sW6Lz9LOb/mGQ62iTv0Ycv5h4xF8OYQe3sWtpV63w77jrY73IUT56MWDUyxfV41gv7RtE7NCzqyjWpxGQUX/AFy+hAQsmk0fQQ01XGWHHaKadrYWOnnrFodeadNTBmvHVk6B4dUepRDfsIJJyQT5keMGEFFRUUyr+6NN97gggsuAOCII45AVdWUHfTvC62NlGkrCEpxyueCUoxlyWBbyUE3gR2pdIpiOHynEW1jl+16dwZy25bT9iHbdnLVHHaPpRs/RTN1Duk9uN1nfreXwUUH8ObaJcmFYi1ssDGm08duYKa7knNytrO9dCxbx95BjvENlixSUx4iM7/z1WTzFRmbVC1yQTRRPPVowXwO9Wzm8/r+fFtfTF5uBbm5jaguGDhwMxneGPkug2vOPoaoYbNK7E/55k0EGlpqMtiSH8M9ACWwAsGMLyYIsopy6JmYNd+if/HKHj49h66gre1rm2vZGfuZ/HsHttG0FMdJctgjOhxvow0IouC0KQeHndBjZ6XPPvssP/7xjxFFkfr6emzbJjc3N/l5SUkJFRUVHHLIIZ0+Z16erzsuNUlBgX/XB+0Gtu1F6n8vsW9/H9/VU4px9b8X0Z0HsQYET1HK4Ct4ipAUFwX+rr2OztLV9/9d+v3u+O3ubq9t2d/vD8CO6l3arnfWhwoydm89sic8n9b0tOvp6vba0+7vv6/8j1xvFgf3G4CVJjrr2MGHMXfx/1FjVDOs1wDWL9mOT9zIrYGnUZUoc4IjOPzwCRQ1fYokGCBDXVWEnKI+iJ0MWS9wxYfsWsOgxNNS7VX1VROuOZA+chMFchP3fzmOOwe/i25mEYs0ccABG+jbr5GN60oYIFVyyfln89zc7fRiCZvWfsmYk09p+RH1MNjxDZnGJwh5pwJgZ42mdvMytA8WUHz0OCTf7knS9LR3CbvfXnviPUDqde2O/exK29iZa+tp9ORrS8futNf9dW+27UUqm05s07SWNtX7RvSvXsJ16EE99pn31OuCnn1tDl3LPnXIzzrrLHbs2JH2s6VLlyJJcZ3V1157jUWLFvHss8926e/X1gaxrO5ZnSso8FNdHejy88pSEd6y2fHwMlumKeLBCIaQJRX/qOnJ8DTBU4QyajoNYRUj0PXXsSu66/6/C7/f9re7yoB2Z3tty/5+fwm6o1131Id2h57yfBJ05fX0xPba0553ZVPN/7N352FyVOWjx7+nll6mp2dfMkmABAiQEJAtBAFBRRE1ElAQRHC5IlFUUAEB7y9hCYpsIghoQH6ocbuCskVEDCAKCElYlBAQgUC2ySw9W09v1bXcP3qmM2uSyfRM10zez/PwADXdVae633q7Tp2Nv659htOP+BhtHUPHzgG1s1BK8ftnH+X8932WzY/fzWWpFcT0Ui7ccCh7zjyA91bWU/vGHdjKxNAt2lrSVO1TsdOfW1XPb2SzlcV1w/ntwbJNJJpnk+qcwtHh//BgfB5rt5YzNZCbMb2jo4zKykY8rRbVtJa5R32Eo947n84H/8QTj/6Vg957fJ+jlBAK7o3e+jRd5hF4Wm6MsnbQQty/3MDmB28mfOIlO/3Z+TFWYWTx6rd47DWwXCPNn4XIjTtbNj8Zr7IVI16L/bkHA1OJzPwhWDG8dAfZ1+/H2O8LEKzwZTwU+/PanvEum1T+i2tcK+T333//Dl/z17/+lZtvvpmf//zn1NTUAFBZmXsa39bWlm8lb2xsZMqUKcPuZ7KwHY/ORKjPFi+/Pa5PIXL0rWiejW4Gcz+60h1ITGC9cV1x3O042QyuMohb4VHF9XDXkBA76/6XH8PxXI7Z+7BhX1MWKmX/upmse/VJ4uufYY/kGzxjHsI96QhvZwxO2H8WZJNMc9fRHphCLRvIWEE0Q9/pMeSVpo6CQTOtB6JbUXqGdNseHLnXi/wpfhhPxvfns9WrAGhvj1JV1UFdfYLNGxVm07/42Cmn86tn76V889/YsP4d9pw5I78/KzyXkszbBJNrSJceB4BWVo+x33Fk//1nAgd9Ar3hgBF9hmLsDbwv2FH+lNwoCi1juThOGZGAiVZSg3bgAcStMJXKVyNkxW4im81yxx138MgjjxAIBNB1naOOOopvf/vbfP/732f16tVomobneSxatIhPfOITPP7446xZs4ZLL70Uz/O49dZbeeyxx9B1Hdu2Of300/niF79Y8LL6qsv6k08+ybXXXss999zD9OnT+/3tpJNO4ne/+x3nn38+a9asIZ1OM3fu3CKV1B9sx6MzlfsxrY1Gi9IyLkSh2Y6HClXRno9nuUkUxWO7Dn98+S/Mn/keSoORYSvPejrO11SSuYl3cNIR7jXO5tGyQ2jcvBylytn/kCOp3vIgAS1LN0FqASNUvdOVcQBDKSoNnZYBFXKlPELlG0m17cWUPddwRMlb/CM+i5PKX6XaSJDJBMhkQtRUt7B5YymBxjVkp83nAwsW8uayv/O7+x/lO9/+Sn5/rlGFHZxBoOsZ0iVHgpb7nTHnnIiz4QXSj/+IkrNuR2n6yD9QMab63hfkSP4U40tiUPjF5ZdfTiaT4Q9/+AOlpaXYts0f/vAH7rrrLjo6OnjooYfQdZ1EIkFLSwsAJ5xwAieccAKQm7/sueee449//CPBYBDLstiwYcOYlNVXj6wuv/xystksF1xwAQsXLmThwoW0t+cmnLnoootYtWoVJ554IldddRXXX3892gRaPF7XNYKaThCdoKaj6xOn7EJMRHLNiUJ4+s3VNMVbOWH/owdXnl2Hkq2vMWXVr9j7kat5T8d6nnCivND1bR4uPZSDyxzSXSXM3KOWQMBk38Sf6XQrSSVasG2XcHnD0AfdjtqAQXM2O2h7pO51PNck0TKLk0pzy4I+2P6enr8qOjoihEMdRCIOerIZvfNdZhxzKtm6WSTffJFXN7b1258Vmoty0wSTL+S3KTOUm+Bt63+wVv9uxGUX/iC5UYwnXddIdWUk3sSQPM/FS7fhJpvw0m143s4/pN6ed955h5UrV3LNNddQWpqbg8EwDM444wza29upqanJD5WORCLMmDEDgD/+8Y9ccMEFADQ1NVFZWUkgEAAgEAiw7777FqR8A/mqhfy5554b9m+1tbX8/Oc/H7/CFJCua3gJhyeXrSXRliZSFeLYRXPRIzqOU5jAE0JsI9ecKATP81i+6gFqS6uYVTcjVyH3PEJt71C24QWiG1/CyHTjmmGsGYfTNfVQuh8N0hoKkUKhxzeBE+CY+cdQt/F3VBidvJI9mqj5FM2bU5TX7YPtWiMqU0PAYHU8hed5KLVtMjizpI1AdAvxLQdSX/MW7y99lZXdB3NoZCOHlmykszNKXV2M+oZO3n6zisCGf5A66Gzee/JnePFnV3L3rx/ixks/j9azT9eswQ7uQbDraTIl80DL3ZAYex6Ks2UtmWfuwdjrCPQpg2edF/4luVGMp954e2zZvyTexCCe5+J1rSfz/P/k570Izr8GymaiRjnMYd26dey1116Ul5cP+tvpp5/Ol770JZ5//nkOPfRQjjvuOD70oQ8Net3HPvYxfvvb33LiiSdyxBFHcNRRR/Hxj38cwyh89VkeU40Dw1M83fPjB5BoS/P0srUY3s7NrCuEGBm55kQh/HP9S6x59xU+ffhH0ds3U/PKCmY+cjV7PfEjytc/h10/i673fo7WD3yNpqlHsuWFOqa5DTwW2UJAeby2fj2GqfG+fYIclHqAVmcqG9pDVFW7dHWauIx8mcqpQZOE49LlDF4TvGz6ajzXoHPDYXys9GX2MFu5u+UYXk01YNsG8XiUmqpNmKaL0f5f9M53qTj+XAL1Mynd8Bx/eKGx3/5yreRJgqn+65UHDv0kKhQl9cj38azBa1wL/5LcKMaTxJvYrkxHvjIOuWUaM8//D2Q6xvSw+++/P48//jiXXnopNTU1LF26lCVLlgx6XV1dHX/605+49tprmTFjBj/96U9ZtGjRmJRJKuTjwSWfjHol2tIgDweFGBtyzYnt+Pfm17nsgeu578U/59cN78v1XO598c/c8Mfv8dXSEKe+/ggzH/sBVf9ZCWV1xI84ndgHvkFs3+NpN8toe0un+Yl6Mk1htk57i87gdPY1YzS1tfN/Pxzm6KYryRJkXfog4hv/RbTcBKMOdwTjx3tNDeaWO9uUHtxt3Qx3Eql7lVRsH5x4LV+ufIIqPcHNTR/mh1s/xJ/WH4JSLvvM2oLCI/jO47jBKId98ksE3RSPPPAQLd3b9uua9TiBqQS6/gHetu0qGCEw70zcto2kHv1BwboYinEguVGMJ4k3sR2em+23TCPkKuWeO/j3baTmzJnDu+++S2dn55B/DwaDHHvssVxwwQXceuutrFixYsjXGYbBEUccwaJFi1i+fDlPP/00HR2Ff2AgFfLxoEGkKtRvU6QqJJ++EGNFrjkxjNbuNr7ym//hsXX/YOmfb+N3Lzzc7+9uoo2//P671K+8kV/QxlnZNjTdJPGeT9D6/q/TPPsjtEfqSVoWiS0Grf+YQufL1Zghj+lHNlFWH+OChr9xWd09PHvmK3y2/mm63SrWpN5HrL2bUHotAFrJ1F0q/9RArkK+OTN0V/dowyvogTjt64+iDIsLqx9hQdmLNGWj/Hjzh7n1tY9QUdZC5b4x7M6NGC3rCB3/VWpmHcTM2Cquuu9lnD7LK2XCB6M5cULdz/Q7jl6/P+Z7TsZ+4+9knrxtyAcbwockN4rxJPEmtkNpJipc339buB6lmaPe94wZM/jgBz/IkiVL6O7uBsBxHO69917WrFlDLBbLv/bVV18dNJk4wNq1a9m0aVO/15WXl1NWVjbq8g3kqzHkk5WtPI5dNDffbad3DI2t5AZGiLEg15wYzv8+ex/pbIYfnn45y5+5j9/89U6O9izq0x3Y776Au/V1jgFiwQip/d9HonImXd2lZOMKrwt0J4vuOnidLmYmzvSqzdQdvImy0GYiWowjlEfG1XixqYTXnaN4C49ZdilZq5vYf9cy/0CNVFKjs3PXmogqDI2wptiYHrpCrjSHihnPEHvjI3S8M5/KvZ/hxMi/+XDJv2l1y3itrYHfvHEsZ856Bs9wuHftv5k2p479zl9G7JL3o7/8AHfPms55x+QmnIuHZpGxWyjv/DtvR47BMOupdjrQcTH2Ox4v3Yn14v3gOgQ/eIHMvO5zkhvFeJJ4E9sVrCA4/5rBY8iDFQXZ/Q9+8ANuv/12PvWpT2GaJq7rcvzxx2OaJtdccw3ZbBZN06iuruaGG24Y9P729nauuuoquru7CQQChMNhbr/99jGZVFx5u9Fj7VisG9cdm9OtrY3S0jL8smO6ruXGzLiAlktShZzQYkfHH2u78/EHHru2NlqQ/Y5lvA5U7O9voEKUp5DX3GT8fPruqxAKGa/DnZ/neXidW3C2rMPtbMRLdeKluvAy3XiuA54LrgueA66T2+bauLaN69jYdob2eIywYVKi6xjZ5LZ9o2OH9+LfmTJeTkeZv+fRlGS6iKoWKsu2UFHWSDjciaEPHvftYpBWtbyQ2ItfvzuV515cT2V1Pe87YW+63/gzx1VMIxlrovHp3/KN7x1CR2Iv/vsGu9RlHeDGDS102Q4/mLXHsK+JNx5Ed+OhROpfp3yPl1Ba/zHnTqnOIXuvZ0Oimq/881x0I8K82Eq8h67g7bKDOf4L3+bQ+e+jy6zBtLt474aL0bwsz+35A1y9kr2yW6hyOsHzsF/5E9nXH8fY6whCH7scLVKVP44fYxVGFq9+u/577Wq5xvp+ZDRlGw/jVbZixKsfP3dd1wgbBtmMM2bxtqv8+Hn1Gu+yFTJeR8LzXMh04LnZXMt4sGLUE7pNRNJCPk4cxyV/O+SPPCTEpCbX3MTlWo1omXdwvCk4zRtxGtfhbFmH07gOL9ln7FYwghaK4pkldGUcsqE0Di6JTpeKMpfKSpdIiUc4lKunWzaEUmV0pRRNSY1YqhzbLcX0aqitn0q0TucQs4v3uRuIsgxN5QLHciNk1BS61HRsFcTTTBzPwHYNHE8n4wT4eyzKU02dGO2vUFNZx0kfn89v/34tF047BK3zFRrUWj566VxQJo3NFUD7Ln8+B5QE+UNLJ+1Zm0pz6J/x0imv4NpBEk1zSLbsgx7sRg8k0c1U7t/Bbt6yFLPnvMFdR/wvS//1GZ4sfT9zDj6bvf/9K164azFb1n+Bj87bl4psIxvSDewTfZX5b13IC+YZ/Gevj1NlVDHD2kzo4AWoaC3Wi3+g+2dnE5x/FoHDT0eZwV0+RzF2JDeK8eQ4LuGqIN0tcYk3MYhSGoSq2N2n+ZMKuRBCiKLzHBtizxFI/pZUYAMhwHM8UuttMussvMBUjOnvQaveE61yOm2qjMff7uJv/9lKufdvLjrhHWbVZXI7c4AORXNbOesz1STjYbxMkpQVZ8+IoqYyzd4NCXQtCySAJuBVbFeRsEtIqBreje9DazJMlxUhHAhTbjpkzCi2p7A9RcbTSboGW9M63e4mzpizki/Pz7W4u95/aY2v5cR5BtMqn6aq1sB1ptKdKuPtzTPoam9HG8Xdx+HRMH9o6eTvHXEW1lYO+RqloHyPNYQqNpLu3AvXiuBYYbLJStxsGFB0rD8au/15Dj72LyyeupJHXj+Shw/6Iu3BOvZ+6Sc4Dy9lRZ8h9lPqI5xz9kG8t/znvP7IzayP1/P2lBPYZ/Y89p55NKGavbFffYTM03djvXAvxv4fIHXUJ/BCe6CMwK6fsBBCCDGJSYVcCCHEmHC7Y2Q2vkGyuRvXstCdrXjpTtx0Ci8dRxEnWttFsCRBoDqDWaPhKY/Mu+BmS9CrILxPivC+Jp0d0J3YSDK9EavRQsfi4zU25+xnE4y4YAOtinhnNVqlQ6S6nbJyj4R+EJ6xN1knjN5p05p12RB7E9dOYnpJvHQX3U3r2bJxA9m2VkJOkvzy3goUinYFWwArWE6qpI5UoBKCQWorHU48IMnsPR02ter84Zm9KXFs6sw32GPPBPvtGWDLu0nefrMao3wWyWQK127r2feu18gbgiYHR0I82NJBhWFwXEVpvzXJ+wpGmwhGB8xi6ymcTJRspoKWtgo2vHkAex/wb84s6+CYdw7gkbmH8MjMB6h84wnSW1/D8Vyi4QjxigjL1mh8cN9GDpoJc4JZ4FE6Nz9A7HVFigo8s4LgvnMoSXcQ2PhnYu/+CZSOVzodLzITVVaPCpWhgiWoQAnoAdA0UBpKaajSavTafXb5sxFCCCEmmt2qQq6NpknCB/uX4/v3+GNx7PE+n2J/fwNJebZvIpQn8aelOBv/hQlopqJ+XhT6TE7qRT2o7hkXaWvQrlBxRchTOMoh3arobjUJVruUVXZSMURjsJ0O0Nm4BxlnNkZNOW6pItWp0bapnfLoK+wZfRDd6TlGec+baoY6g4qef3ZGqucfaNqY4IH/3cyap7bi9hynvLaGuj32YJ9D57D3QQcytd5j0GwtChjFkPsr9kpz+XOr+NnmFk466FBqwqEdv2lILrAPXekwlVPeoGbqYxziKeqe+ip/nf1hWvf7MFZsEy1tjbwbbycdt7lnUxma8phbleArh6Q4oKyLcLiDKSVxSsJ91yXvW6YWml/rwnk9lhvrPyxF2TceQAuXb+c1ozfS68dv11svv5YLpGyFNJLy+vXcpFwj5+eyicLarSZ1E0IIIYQQQggh/GL3m8ZOCCGEEEIIIYTwgd2qy7oQQgghhBBCiMntz3/+M8uWLcPzPDKZDAceeCA33XTTsNuLSbqsCyGEEEIIIYSYFJqbmzn55JO5//77aWhowPM8XnvtNWpqaobcPmfOnKKWV7qsCyGEEEIIIYQYV57n4mbbcK2tuNk2vO1O+rnzWltbMQyDiorcZK1KKebMmTPs9mKTLutCCCGEEEIIIcaN57m46fVk1v9fvOxWlDmF4MzvoYVmotTo2owPOOAADj74YN7//vczf/58DjvsMBYuXDjix79CAAAgAElEQVTs9srKIZZxGUfSZV0IIYQQQgghxLhxs22k/3s+XnZrfpsypxCadQeaWVWQY7zxxhusXr2alStX8uabb/Lwww9TUVEx7PZikS7rQgghhBBCCCHGj2f1q4wDuf/3sgU7xH777cdnP/tZ7rnnHqLRKKtWrdru9mKRCrkQQgghhBBCiPGjAihzSv9N5hRQ5qh33dTUxEsvvZT//61bt9LW1kZ1dfWQ26dPnz7qY46GjCEXQgghhBBCCDFulFFBcOb3Bo0hV8bou47bts2Pf/xjNm/eTCgUwnVdvvnNbzJlyhQWL148aHuxJ3bbrcaQx2LduO7YnG5lZQnt7ckx2bcc39/HH3js2tpoQfY7lvE6ULG/v4GkPNtXyPL4MV799nmPhd3hHMGfsQoji1e/fld+LRdI2aA48erXz13KNXLjXbZCxutIeJ6LZ3fkuqkrE2VUjHpCt4lIWsgLxDB0Of5uevxin3sh+O0cpDzb57fyFNpkPz/YPc4RJsd5+vUc/FoukLIVi1/PTco1cn4uWyEppaEKNIHbRLb7PYIQQgghhBBCCCF8QCrkQgghhBBCCCFEEUiX9UnK0BWRQArNs3GVQcIKYzu7zXQBYhKRWBa7A4lzIcR4kpwjhH9IhXwSMnRFVG0l++xi7FQTKlxPdN5S4voUSbZiQpFYFrsDiXMhxHiSnCOEv0iX9UkoEkiRXb0YL9UEgJdqIrt6MZFAqsglE2JkJJbF7kDifGLL2C5PvtWOu/ssWiMmOMk5QviLtJBPQppnY/ck2V5eqgnNs4tUIiF2jcSy2B1InE9sd67azIPrWgkaMzl6r9GvnyvEWJOcI4S/SAv5JOQqAxWu77dNhetxlTx/EROLxLLYHUicT2wbOtIAvNKUKHJJhNg5knOE8Be58iaR/AQd2BhH34S19g7cpmdR4XrMeUuJW2FAutSJiSNhhYnOvw5SjSgjhGenIdwgsSwmlbRTQunRN+Fl2vCsDux3H8XY/wsS5xPE1rgFwKbOTJFLIsTOkd9WIfxFKuSThOe5gyboCBx5Nd5BF+B6irjMnikmKs8i++8f4fXEtXnk0mKXSIiCMXRFidtI5vnF+RgPHHk1CdWAbbnFLp7YAc/ziCWzALR0W0UujRAjIL+tQviG77qsP/nkk5xyyiksXLiQk08+mcceewyA9evXc8YZZ/CRj3yEM844g3feeae4BfWbTMegCTqsVUtwPUVnKiSVcTEhRQIpsqsGTDyzSiaeEZPHUJMrWauWENKTRS6Z2BmprIvV8/vamZbxt2JikN9WIfzFVy3knufxne98h1//+tfst99+vP7663zmM5/hQx/6EFdccQVnnXUWCxcu5MEHH2TJkiX88pe/LHaRfcNzs/nEmt8mE3SICU4mnhGTncT4xNbRUwkvDeh0ZZwil0aInSN5Rwh/8V0LuaZpxONxAOLxOHV1dbS3t7Nu3ToWLFgAwIIFC1i3bh1tbW3FLKqvKM2UCTrEpCMTz4jJTmJ8YuvqqZDXRwNkbBfLkWEGwv8k7wjhL76qkCul+NGPfsT555/PBz7wAb72ta9x3XXX0djYSH19PbquA6DrOnV1dTQ2Nha5xD4SrMCctzSfYHsncktY4SIXTIhdl7DCEtdiUpMYn9gSVq5VvKbEBKAjLa3kwv8k7wjhL756FGbbNsuWLeOOO+7g8MMP54UXXuCb3/wm119/fUH2X11dWpD9DKe2Njqm+98RvWJv9ONux3OzKM2EYAWVavyeuRT7/It5/LE49ljH60DF/v4G6i2P5xU3rgeWxy/8Vp5Cx6vfzm8s+C3Gx4ofv8uRxutw52DEckueTasqgY1dqKA5rufrx8+2l5StcEYSrzt7buOdd/z6mfu1XODvsonC8lWF/LXXXqO5uZnDDz8cgMMPP5xwOEwwGKSpqQnHcdB1HcdxaG5upqGhYUT7j8W6cd2xmdystjZKS0t8TPa9s8dvbU0AZs8/QHz81kT1w/kX6/gDj12oBDqW8TpQsb+/gQaXpzhxPXx5iquQ5fFjvPrt8x4LfovxseLHWIWRxev2zqExlvuuIrkOfLy7tYvacbqz8vN1ImUrTryO/NzGJ+/4NR78Wi4Y/7JJ5b+4fPUIfsqUKWzdupW3334bgLfeeotYLMZee+3F7NmzWbFiBQArVqxg9uzZVFVVFbO4QgghhNiNJXu6rAdci/aXHmPNG+8Ut0BCCCEmHF+1kNfW1nLllVdy4YUXopQC4Pvf/z4VFRVceeWVXHbZZdxxxx2UlZVx3XXXFbm0QgghhNidJbO5SdyamhpxrRS/XfEYX/ngwUUulRBCiInEVxVygJNPPpmTTz550PZ99tmHe++9twglEkIIIYQYLGE5BA2NWFs7wLgNMxJCCDF5+K5CLvrTdUWYJJqbxdVMUpTgOPKDLyafvrFuxy10PSixLoYluVH4QcJyKDE1Wlo7AEhnMniel+/lJ4SfSN4Uwp+kQu5juq4IpzbTvPxi7PZGjMoG6s65kVR4miRQMalIrIuRkHgRfpHMOoRNne5kCsitFpPMZIiEQkUumRD9Sd4Uwr98Namb6C9MMp84Aez2RpqXX0yYZJFLJkRhSayLkZB4EX6RtFxKTI14T4UcoKWju4glEmJokjeF8C+pkPuY5mbzibOX3d6I5tlFKpEQY0NiXYyExIvwi0TWIdTTQm6GSgCIdUmFXPiP5E0h/Esq5D7maiZGZf+11o3KBlwlIw3E5CKxLkZC4kX4Rdp2CRmK7lSaUEkpAF19WsuF8AvJm0L4l1TIfSxFCXXn3JhPoPnxPpQUuWRCFJbEuhgJiRfhF5btonkuVtamJJKrkHcmpEIu/EfyphD+JY/FfMxxPFLhadSddzeaZ+MqQ2bEFJPSwFjXA0G6sjLLuhia5EbhF2nbxXOyAJREIgDEE+liFkmIIUneFMK/pELuc47j0U24z5adT5yyvIWYSPrGem00itMSz/9NYln0JfEg/CJju6ietccjJbmWRumyLvxqqHtKyadCFJ9UyCcpWd5CTBYSy6IviQfhJxnbBccFIBQwUbpBPCkt5GJikHwqhD/IGPJJSpa3EJOFxLLoS+JB+IXneWQcD9xcl/VgwETpJp1SIRcThORTIfxBKuSTlCxvISYLiWXRl8SD8AurpwXRsXMV8pBpoHSDThlDLiYIyadC+INUyCcpWd5CTBYSy6IviQfhF2k711XddXKVl5Jgrst6dzpTzGIJsdMknwrhD1Ihn6RkeQsxWUgsi74kHoRfZHoq5L0t5OGAgdIMkmmrmMUSYqdJPhXCH+QR2CQly1uIyUJiWfQl8SD8Il8hz/ZWyE3QdVJWtpjFEmKnST4Vwh+kQj6JjWbJNCH8RGJZ9CXxIPwg0zO7um3baJoiZGgozSBjyYRYYuKQfCpE8UmXdSGEEEKIEeptIbdtm6BpYugaStfJSAu5EEKIEZAKuRBCCCHECPWd1C1gGhiKXAt5VirkQgghdp50WZ+gDF0RCaTyY348L1LsIgkxJgbGesIKY8v4NlEAEltiNDJ2LlZsx+mpkHsoTSebtcl1+1VFLZ8Qu0LyohDjTyrkE5ChK6JqK9lnF2OnmlDheoz512Do9ZI0xaQyVKxH5y0lrk+RWBejIrElRmvbLOs2AdPMtZDreu5v2Vw3diEmEsmLQhSHdFmfgCKBFNnVi/FSTQB4qSYyz/8PkUCqyCUTorCGivXs6sUS62LUJLbEaPVO6uY4DgFDRwGanmvnSGWk27qYeCQvClEcUiGfgDTPzifLXl6qCc2zi1QiIcaGxLoYKxJbYrT6TupmGgYeYPS0kKcsWYtcTDySF4UoDqmQT0CuMlDh+n7bVLgeV8kIBDG5SKyLsSKxJUard1K3bE+FHMj/W1rIxUQkeVGI4pAK+QSUsMKY85bmk6YK1xOcfw0JK7yDdwoxsQwV6+a8pRLrYtQktsRoWX1ayANmrmXcyFfIpYVcTDySF4UoDnnkNQHZjkdcn0Lk6Fvzs2CqSDV2a6LYRROioIaK9bjM+CoKQGJLjFbadjE0hWXbmD1d1U1TKuRi4pK8KERxSIV8grIdj85UKP//taXS2UFMTgNjPbeckBCjJ7ElRiNjewQNjXjWzreMB4xcxTwpFXIxQUleFGL8SS1OCCGEEGKEMrZL0FBYWRujpyIe6GkhT6QyxSyaEEKICUQq5EIIIYQQI5R2XIK6hpXd1mU92NNSnpRZ1oUQQuwk33VZz2QyfP/73+ef//wnwWCQQw45hKVLl7J+/Xouu+wyOjo6qKio4LrrrmPGjBnFLq4QQgghdkOW7WLqiqw9uIU8lZZZ1oUQQuwc31XIb7jhBoLBIH/5y19QStHa2grAFVdcwVlnncXChQt58MEHWbJkCb/85S+LXFohhBBC7I7StoupcuNre9cfD5m9LeTSZV0IIcTO8VWX9UQiwQMPPMCFF16IUgqAmpoaYrEY69atY8GCBQAsWLCAdevW0dbWVsziCiGEEGI3lbFdjAEV8oChAYrulHRZF0IIsXN81UK+ceNGKioquO2223j++eeJRCJceOGFhEIh6uvr0Xt+8HRdp66ujsbGRqqqqnZ6/9XVpWNVdABqa6Njun85vn+PPxbHHut4HajY399AUp7t81t5Ch2vfju/sbA7nCP48zxHGq9DnYOjFMHcbQkl4QClpUFKu0HpOpbj9HuP52TIPHsJ5gFfQK89bFRl31G5/ELKVjgjiVe/npuUa+T8XDZRWL6qkDuOw8aNG5kzZw6XXnop//rXv/jKV77CLbfcUpD9x2LduO7YLN9QWxulpSU+JvuW4/v7+AOPXagEOpbxOlCxv7+BpDzbV8jy+DFe/fZ5j4Xd4RzBn7EKI4vX4c4hkbYp12wAXNejuzuDm3VA02nrSvV7j9b5CuG2tSTefIQMswpyDn6OISlbceLVr5+7lGvkxrtsUvkvLl91WW9oaMAwjHzX9Pe85z1UVlYSCoVoamrCcRwgV3Fvbm6moaGhmMUVQgghxG4qY7toXu6+RNdyTeWmBkozBq1Drne9lvt35yvjW0ghhBC+56sKeVVVFfPnz+eZZ54BYP369cRiMWbMmMHs2bNZsWIFACtWrGD27Nkj6q4uhBBCCFEoGdtFeS4Aup67nTK1XJf1ZLp/hVyLvwqAyrSgZZrGt6BCCCF8zVdd1gGuuuoqvvvd73LddddhGAbXX389ZWVlXHnllVx22WXccccdlJWVcd111xW7qEIIIYTYTaX7VMh7J3UzNYXSdFJWn2XPPAe963Xc6H5o8TfQO9fi1tUXo8hCCCF8yHcV8j322IPly5cP2r7PPvtw7733FqFEQgghhBDbeJ6H5bgot7dCnrudynVZ718hV5kYyk3jVB6Oir+Jlt5YlDILIYTwJ191WRdCCCGE8Lus6+F6QM8YcqOny7qhAZpBpm+F3O4GwNPDeGYZKtM63sUVQgjhY1IhF0IIIYQYgYydaxnH7amQG71jyFVu2bOsve3FTk+FXAviGWWoTGxcyyqEEMLfpEIuhBBCCDECGTu3BJXbWyHX+ndZz2QHt5CjhXIt5JZUyIUQQmzjuzHkuzNDV0QCKTTPxlUGCSuM7YzPOtRCTDRyvYiJZGC8el6k2EUSo9DbQu71VMjNnkndjJ4Kud2nhby3Qu7qIZRZDt1vgOeBUuNcaiFGTn5rhRh7UiH3CUNXRNVWss8uxk41ocL1ROctJa5PkcQnxAByvYiJZKh4NeZfg6HXS7xOUBmnp0Lu5CrkmqHheaAphabrpO3BFXL0MJ5ZjnItlNONZ0THvdxCjIT81goxPqTLuk9EAimyqxfjpXLrk3qpJrKrFxMJpCjVU5SpLkr1FLouT9TF7kHX1bCxv73rRYjR2l7s7Yqh4jXz/P9IvE5gvS3kjpOreOuanv+brhu4rovT03qOHcdTOh46nlEGIN3WxYQgv7VCjA9pIfcJzbOxexJeLy/VhO5maLzzq9jtjRiVDdSdcyOp8DQceTIpJjHPdQmnNtO8/OIhY3+460Xz7GH2KMTO0XW13djbFRKvk09vhdx1HExDp+8jm941ydNWlkhIR9kJMEoB8MxyADQrhlsyYzyLLMSIaQyTu5DcJUQhSQu5T7jKQIXr+21T4XqyLRuw2xsBsNsbaV5+MWGSxSiiEOPGSbTnK0QwOPaHu15cJc8YxeiESW439naFxOvk09tl3bZtAqaJ53kEtthoKRfDyH2vaStXaVF2NxgRwMMzcy3kWqa5KOUWYmS0IXOXVB+EKCy5onwiYYUx5y3NJz4Vridw5NW0Pnpn/jXBPedSd+pFBMws5eE0hnRfF5OUZ1v5ClEvPVpNIOBSGeoGpWMe2f96MectJWGFi1Fc4ROGrigPp6kMde9yjtTc7KDYs9sbR9WaPVR+D86/RuJ1Asu3kLsOAdMg+N8M0bUZSl/JYBjbWsgBlB3H00tybzRK8VAoS9YiF/7nZMGYe3G/3GW851JQ2qjyrBCiP3k87xO24xHXpxA5+tb8TJZZR0Mvrab+nBswKuoxwxrWC1eReV0m1hCTl64rUIqpX/0ZTnc7HU/9AoD6T16I9ey38HomltHmX4dx9K2onuslLjO/7tYKNfmQq5kYlQ39KuUlc45HaRplXheuZpKiZETd14fK7ypSjd2aGNE5Cv9I91TIbccmYOiYW3tmW+9wCVTkKuTJVAqoBDvR00IOKB30EpTVUYxiCzEirqdoW/kbKuYvwigtx81mQQuRfeaC/G9x2ZFXk9WrSDpBGU4pxC6SFnIfsR2PzlSI9nQpnakQGUJUnnAusRU/xEs0Y71wlUysISa13vG7W376Zbb8JBf7VR85n9qTL8B+5fr+8f/8pXiQv16kMr57K9TkQylKqDvnRozKBiBXGa884Vwal32ZTTcspPnOLxFObR7xRG8D87tS8vM7kVk965DbWQdTN9ATLqn6XEU8gM6+HS/y+/9zEC88dHeuhdzY1hvCM0pR2failFuIkUhRQsUJ59F8/01svPXL4NjYL/W/F7VWLYHW/+xSXhRC5BT8juDNN9/kwgsv5Nhjj2Xu3Lkce+yxXHjhhbz55puFPtSkF3ASNP3qO9jtjeil5fkE2EsmBRKTzVDjd1vuW4pZOUXiX2yX5tkFiRHH8UiFp1F33t1M/85DVJ98ST4Pg8zlIXL6tpCbPbdSyWpwDAhbNrPb/4nnZHl82WLIdoPev0KO1VmUcgsxEgPzoVk/Y+g8GwpLXhRiFApaIX/nnXf49Kc/TSaT4Vvf+hY/+clP+OY3v0kmk+HTn/40b7/9diEPN+n1HcvodHfKpEBi0htu/K43zMQyEv+iVyEnTnMcj24nTJcbxXOcgo8pH06hl1sTY8fqM6mb4eVupTJBsCIQbX+bgJuh4bjPkE13o7lJ0EL59+ZayKVCLvxP1xVhkmhuFlcZOJhD5lmnu3PM8qIQu4OCVsiXLVvGwoUL+elPf8qnPvUp3ve+93Haaafx05/+lFNPPZW77rqrkIebFHRdI6jpBNEJajq6vu0r6R3LCBBb+fNBE2sE5l1FyikpSrmFGBP6tpjvZVQ2YDvmoEmxBk7itr1rSUx+Q02ctqOJ/nYmZrxhYtLTCvswKL/c2p1fGlXXeDE+0raLpiBr25iehquDa3hYJYpI+1sAhPY/lpkHHQGA3WfKHs8ohWwneDLMRvjXUDnJTSYIDJhQ1Zh7ce4etbJBHpILsYsKeuWsXr2ae+65Z8i/ffGLX+Rzn/tcIQ834em6hpdweHLZWhJtaSJVIY5dNBc9ouM4LilKqD/nRpqWX0xmw1paV/6G2pOvw0vHsTtbaH54GRULLsVCZuoVk4PSFHWnX0HzvVdtWwP69CtQuMS9/pNi9Z3EbUfXkpj8hpo4bXsT/e10zGj6kDGJphe0/MMtt1Z33t10S473nYztEtQ1rKxN1A3hhhV4imwYQt3vktRLSRLkyONOAp7h3ddeZ49jj8q92ShFuRbKTW2bfV0InxkqJzX97zeo//LdaLMvwSivIduyiab7f4wTj1F/zo0kKQHkQZMQI1XQCnlbWxvTp08f8m9Tp06lvV0mMenL8FT+ZhAg0Zbm6WVr+cC3DsEh121SlVbT8MVbUIZJpvFNtv56CZkNa/P7qPr4RcPuv19Xo12YGViIcWdniD16G9ULvo1eUoaT7CL26G3Un3kNEc0h5USxrN7K0rZY3tG1JCaX4XJb78Rp2wyf73YmZnRdodkpmoeIydozvw8ECnZOY7Hcmhg7GdsjYGjEszaGo3DCCg/IhsFINdNhlhPP2NTvtxdknuGFlX+GmiOYNuuAXAs5oLKdeEoR6PoZrjkTO7KguCclRB/D5STlWmy67TyCe86l4vjPU/3Rb+Aku1CRSsJuEk3JPacQI1XwviVKDd29TtO0Yf+223LJ3wz2SrSloae+oesKrzvG1uUXU73g28RW/LBfctxe96B8V6Oep5tGZQN159xIKjxNEqTwL83AicdoWn5JfpNR2UCm8b/EVvyQ+rOvh+iefSrlPXZwLYnJo2C5bSfybzi1mWy8ZciYLHTXzKGWW5MuoP6VcVyChiKWzY0hd4K5+xs7BCrdTNKopjvjoJN7oJLOeNxz+SJ0w+QTZ5/C/H1By3agWysxko8BEPdmEC6dW7RzEqKvYZeAVD2/yxvW5vOiUdlAw5d/QvNdX5V7TiF2QUEHWabTaT772c8O+c9ZZ51FJpMp5OEmPg0iVaF+myJVofy3EiZJU89NZ8dTv6D2tMX5sYz5ZMfQ3d2G6/4oM2AKP0tpUerPvr5fnNeetpiOp36R6y73q+8QduOD37iDa0lMHgXLbTuRf5uXX0z74z8bUe7dVQOXWxur44jC6O2yns3mZlm3zdx2z3Ug3UrSLCORddG93H3PB84+nw999lz2Pvgwnnv0kZ6dtGEkH8M2DyDjwJMvXkUqmx7miEKMr6FyUtVHv0nsTzcPyon159xIbMXNcs8pxC4q6KP3733ve9v9++mnn17Iw014tvI4dtFcnh4whtFWuaeJfbsLZTaspe0vd1C94NsEGmbhqMB2uwMVuvujdH8X48GyXIjuydSv3AWOTabxv7T95Y78MI1cdzmbMtXVv6vyDq4lMXkUKrf1xsyrf1rPfu+NEo5qBMvCZAwPrG3Hsdsb87lXLylDr5hKN9GC57++ywv1joGXPOtfGdslYGhksllMU8fpqZA73S2AS8KsJGU56F4KAL2knL0POZw9DzyYB29eAoCWfR3ldbC2+zDi7a9xUHWK1e/+m+P2PbJIZyXENr05qX7R3SgnS2dTlkzSIbnuKZzuWL9hPHqknOS6p/q9X4bcCLHzClohP/XUUwu5u0nPcVz0iM4HvnVIrpuklrtJ7J1QaGB3ocyGtcRW/JCGRXeR8rZ/o1bI7o/S/V2MF11XBJwEKA80fchhGp6TZdNNpw+Iw+1fS2LyKFRucxyXQJnOkZ8I07T8QhJ9cpsTnobLtuP0ds00KhuoO+/uMct7juMNmMBtx8eRh6XFkbFdTA0c18VUOk5P+DldWwFIBSpIWA46uRZy1zOxNZNExUwqps/C8zyU/Rbo8I9NGay4x0WHazz8xotSIRe+4TgeccLoeoSSaYqA1zV0d/X/c6sMuRFiFAraofOBBx7Y4T+iP8dxybgOGRwyrtOvAjFUd6Ha0xYTe+iGHS6HU8juj9L9XYyHvkusbPjBJ2h96MZB3dfrz76O9qeWA4PjcHvXkpg8CpnbAk6CpuUXDZnbJkIXclkqrXgyjouucjnGVBpOz2fudOZiKRmsIpX20L0UrjJx0dhc/0Gaat5LeM4HSSSy4GzGMxpYs7mJuFMJQDr5RnFOSIjt8tATG4k9dMOg7uq1py2m/anl1J1+ha/zpRB+VtBHV7///e+H3K6U4q233qKzs5NTTjmlkIec1Hq7CzUsuguncytOd3u++67V+MZ2l8MpZPdHmf1XjIeBD356u79NXXQnnuugNJ22lXfRveah/HskDnc/45XbHNf/XchlqbTiydguUSMXC6bSsc3cf9vxJkCRDvZ2Wc/gqhCJYD2ZQCWak8bY933EY08Q1jvxzKN4relF9tl/T6CTsNc4/EGFKJK+uSbfXb20Ej1cRvN9V5PZsJZs89s0nHcnnuf5Ml8K4WcFrZD/5je/GbTt9ddf55ZbbgHgoouGX6JLDM1xPDzlsOUn5/bbbrc3ontZynqWl/DcyJDvHWn3x6HI7L9iPAxVOUquewp3wUV0ueWUqhTpt1b3+7tR2YCnGZSqlHTZ3Y2MV27b1eOMphv5SN4rD0uLJ2N7VOjbWsg93QNP4SY70MNRPD2AlXXRSeNpYbrDU9HcLFManyA17XDSTTaa7tBNA+2pBCXhWtLOf6kPJ3Ld2WVVGuEjA+c06u2uPnXRMgDqz7kBvbQSlCKRn2NDfoeF2FljNgfxO++8w7e//W0+97nPceCBB7Jy5UrOPffcHb9R9KPrCqXr+W5AvYzKBrIt6/PdFK2mN8esm+JE6LopJr7eylFfRmUDChddV0PGYf05N0E20a/LbklqE9GAJd12xQ6NRW4bqht5SWoL0YBFmeqiVE8NG5sj7YI+3DUjD0vHXsZ2UV5uxXozYAK578hNdWCUVKB0Hct20L00rhYiEW6gJL0VLdNBOFKKVpK7/dqczM30X1NSSmumhH3KXTrTXUU5JyGGM1yuwYOqj5xPbMUP2fKTc2lc9mUZNiPELih4hXzLli1cfvnlfOpTn6KhoYHHHnuMr3/965SWlhb6UJNe782Z3dE4eGzO6VfQ/vjPgFyLyNZfXDRmY7r7dhGd/p2HqDvvbpnQTRRcihLqh5ozYcXNhEkOGYeUVtP0vxf067LbtPwSnMbX5KZgEtN1Rame2mEFd0fGIrcN1Y28aflFOI2v9atke+7gOQ5GOl+HPCwtnozjovW0AJoBHa8nZNxUB2akHF3pWE5u2TNbhbCNUrYrrDoAACAASURBVELpFhQQzLQSqAjhufB2R+6NVSUlxO0o+1dCU1esSGclxNCGzDWfvgq9oo6W+5YOylmlqlt+f4UYgYI+Rr/66qt58MEHOfXUU3nssceorq4u5O53G4auiARS6GTJtsdQwQpaH7x+27I70Rqaf39FfikoGPtuioXqIirEcBzHQ0Uq+i2l0jtnQtWCi/Kv6Y1DQ1eU6d00nHM1TncnsZU/J7NhbW44R0k5TTKWdlIY2IXb0iMEujcVbNWHQuS23pyteTZKKfRodb+u5HZ7I2bVNKYuWoaT7KJj5Z3UfvK7QKDffkbaBV2WSiuejO2i9bSQG+a2Wyk31YlRMw1N17EzaTQvRVrfEwAz3QpAIB2jpMpAZaC5M7etNlKKla2kKtTIi/ENUL/3OJ+REMNzHA+rdDrTv34XuBae7dD2xHKih318yJzldscIa53SeCPETir4GPJwOMxf//pXVq5cOeRr/va3v+3Uvm677TZ+/OMf8/DDD7Pffvvx8ssvs2TJEjKZDNOmTeOGG26YlBX+QECj1NuC9ewS7FQTKlxP4Igr0aPV+TE79efcgBPv/wRduimKyUB5HrEVP0SPVlNx/Oep/ug3cK0UStfQlcr/sBu6Iqq2Yj27GK/nOqk/9WKa7v8xTjyGFi5Dj1bLWNoJbqglF+vPuZH2lXf6ZiKz3ljMPrs4n7OnnH45VpeF0hROsov4CyvItm1m6z3fzPf88Fxn0L52Zb4OeVg6/lzPw3K8/HdoBPRtf0u2o4dmo2saWTc3qVtGLwPPxbTa8IBANkZpJdAF8e5mwmaAiBlAeVUAJBPrgfeP+3kJMRxDV0TcRqznt/3mVh59MdmkN2TO0kKlxB65lcpTvouHQ7a9m1JdlweGQgyjoDW4X/7ylwXZz6uvvsrLL7/MtGnTAHBdl0suuYRrr72WI444gjvuuIMbb7yRa6+9tiDH8wtdV0RUB9bzS/BSTQB4qSasNVdSd8oNbNryBnZ7I/EXVlB/9nU0/erS/E3qlM/fRIIS5GZMTGQeivov3IyX6KD53qu2VcLOvp7SqE43YRzHIxJIke2pjEPuOrHX3kjNSd/A9YzcjcAJ56K0/hV5MbGESdKx8s5+vSbaV95J9PAF+Vn4obgTmQ0Vi9mXr0Xb5xtsXnZBfrm+zn/ely9ry31LmfqVu4Zs/a8758bBrf+S230lY+eGGygv92/DzFXIPdfBTXWhhSJoSqGpLLrKktHLMe1u6BmmUOatR9OAjCLd3cqUaDmeAtPMNTI42U3jf1JCbMdwv7n67EsG3Y/Wn30dsUdupeyYM6G7hebllwzqzQTs8sSXQkxGBa2QH3nkkaPeh2VZXH311dx000187nOfA2Dt2rUEg0GOOOIIAM4880xOOOGESVchD5PETcTyCa+XqjkUZRjs8fWfgtKx4t10/uM3NJx7OygdR5kEKmroiiWKVPLBRjPLsNh9uZ5CN0M09lTGIVeBaX/iZ9R+4gLKTQuCubSlHfQ17P/+Frf9NSB3gxConU7j8iVkNqyl+mPfIPbQDVR86DzpNjdBacqj/Jgz8mMUe1uXtZKyfq/rnW2fnmHZfbuQu8ogYYWxt/P9jyZfaZ6NPSBne6km9FCu1ToXv3dTu+DrVB79ifzwCjxvUOt/3Tk3YpVO36ku6CM9R1E4Vs/n7Dq5FnK9p8u6m44DHipYimErQkYGgJRRSTDbkR9nHjU25P4jA253jCllU/A8j6zKxXXQax2/kxFiJ/TNc1rlbIxZn0GZUVS4jvirq2j44i1ghtADGngO1R/6PFrFFNyurdSdelF+SFnz8oupX3Q3XrK9YMOOhJgMClohv++++3b4mtNOO227f7/llls4+eSTmT59en5bY2MjU6dOzf9/VVUVruvS0dFBRUXFrhfYZzTloYUrUOH6fKVc2+MkzL1PIfP0t/LdhAJHXkVw6n4ozcBTGimvhLA2ZhPmj9hQ3Uwl2YqdYekRSmwLPVpNzWlLMMvrQNPRtQTW6su2XQOHXYb97qOYs79E9rW7cdtfQ4XrUXqAQP3eOPEYdkcTyXVPYTW+IWPJJyiFN2jCoJb7ljL1vGX5bpK9k1yi5Voph+pCHp23lLg+ZcgK62jzlauMfjkbQIXrcbo7AYjOO5nqD34aa813tw2v+OR3QDf6TeCmR6tx4y2EI+VoAQUodDy0bAK3Z5WBgUM2dvYcRWGle1rI3WyuV4YeNHDITegGoAXCGK4ialq51xtVmNnO/PtLgs10pcood7sJWHHqa2fl9keAjoyiVJdZ1oW/9OY5FarCnP0lrJduyOez0nlXEl+3iuhBR0OmC8/qwN34KHrgNNz/3gXpNhrOugqrO0vsoRvQ3CyNQ0xeKb/TYndW0Ar5gw8+uN2/K6W2WyF/6aWXWLt2LRdffHEhi5VXXT22M73X1kZ3+b2e62K1tmIDgXlXYq2+Mtfit99ZZJ69pH8X9lVXUHbsj9hyz6U48RhTPn8TnrvvqI5fCL3Ht+MxNt85ONlO+9rPMarGbtx/Mc9/LI491vE6ULHjx3NdrKY3cWybmpMvwbMtGu/+OvVnLsZ54+b+18CLPyBwyMVYL92AedDXyL5yO4FDL8F65TaqP3g22fmfpPWhG4Fc/BnKGfX5FfvzGchv5Sl0vNZUR8i2dQw5YZCT7OzXjT326G3Un3UttbXVeOk20n8f0IV89WIqjrsdFaoadJxdzVee6+Ik2vEcj8CRV2OtWpK/QTUO+g5Nf7yF4J5zqT7xC1jPXdq/q+cr1xM45kf5Ywb3nEvVR84n+Z9/EqqtwVp11bZ9zb2YtpW/oerDiwjU74vStBGfY7H5LVZh5PHa9xy6tdys93rPpG7BkIEXNsGJA2CGo4RsjfJgrsKe1UspySYIhwOAS0kwxqbOfYi6/6FcZaGygtJIbvmz5iaDikBipz8zP362vaRshTOSeB2Lc/O8CMb8a3BTrfnKOPT8Hq++krKjbyTz7MXbHpofegnZt+7DmPUZrFVLsFZfgTn/empOvgRlmEPm9UL8Tu8KP8eCn8smCqugFfLly5eP6v2rV6/mrbfe4oQTTgBg69atfOlLX+Kcc85hy5Yt+de1tbWhadqIW8djsW5cd2xaD2pro7S0xHf5/aV6CuJtKDJkWx7BPOhrKDMKqEFd2L1UE/TcCPQueTbtaz+nPR0YYs/jo+/5l6n00DfRVob2UXxGO3v88Tbw2IVKoGMZrwMV8/PrVaqnaP7FRdSduRQch+Z7r8zNTl1ZT2aIa0CFa/BSTWjRvXKV8p6WcqvrLdR+38qvQmBUNmB7Oh2jOD8/fD59FbI8fozX2too6Y5W3NZ3h5wwyOmO5Se57N3W+x1XhjJD5kzXTmO3bh7UJX1X8tXAVvWSA4+n9pQbQdNwMXCS3TjxGHWnXgTZriHLozwnf24Vx3+ezmf+H7ULL8RaNaDyvvZGKuYvYusvLsq1IDnhYc/RyWZoj/snTsGfsQoji9eB59DYmquQp5O5LumupkinsqQ6cpOt2loIUynKArnf6axeSrB9M6mMRchsQ9OydNtTSSTXUhdQeGaI7kQagGTaZGY0vVOfmd/yUl9StuLE61iem6HXU14aHjL3eJn2/pX0noflufvYnhycbMezsniuO2Re91C0tXWPa09KidP+xxPF459+zsB5553H008/zRNPPMETTzzBlClTuPvuuzn33HNJp9OsWbMGgN/97necdNJJRS5tYWluFr20Ci0Uxm16FmvVEjLPfAuUhgrX93tt7v81qk78KpC7efRsqwilHlrvTMF9ySzwYkc0N4serUYvqUAvrcJubyS451yUERj6Gui5Ntz4u1irlvQbS64Fc61Nsi7zxKW5Wdof/xm1py3ut/Zt/Tk3oFdNG3bt7d6ulX2pcD3Zpnf6rQPeu0buruSrgeuFJ199is13fIWspdOZCpE0aqlfdDfBqbPwrI6h49cI5df11UvKiB6+ADfVMeTNbmDKzH6rBgx3jpJjx0fvpG6ulfs+tJ55LXq7rKtAhJDuUdHTQm4RwevIVbhLgi0AOFod3d0W9WGDqvC2/NSVDVEbdvMP3YXwC9vxhr0n9ayOftu8VBMqUIGXjedf42bSGJVTwLVp+PJPKJlzPEB+bpDYQzf0y81C7E4K+ut9/PHHo9T2L6SdXfasL03TuP7667niiiv6LXs2mbiaiY6DZ3n9xiO6dpLgsdfjZTvw0h3Y7/wFc+/TsBNdGOV1QC6ZKSMAPlnhKUWJzBQsRszVTCpPOJds2ybMmj0pmXM85cecgdXWTODQS7Feuq5Pd7hLcdMdBOZdSfY/23rmaJWzMfb7HFppDTMWryDrmGQIUWomUcrG8wwSaZn8ys8MXeFm2zAiiuqPn0d8zaP57umulYLSWpJOcNiJzxJWmOi8pWRXLx7UhRwGj1fcUb4aavK0Ha0X7jgeng5W87vQ8mhuOEWfMZeBI69GBStIhQPUnXc3Og5oOk5X25Dj0bOtm6k+6et4RhCsoc/RnLeUuBVGcuzYyzi5CrmdtVEAPaueuclcpcQzSggpl7JgTwt5wqRrcylV+7ZREmwm65Zh6Cbd3RZVlSVY0W0tUym3BENrw3JjoNf9f/bePECOstr//jy19T4zPWsmK/uaIFuCJKhXwJ+o6L3h6lXAgLIkQGRPAqhkQ4WQQCAISDBcICrXK4K4gi/gdUnQBAFNICwGSCCZmSQzPTO91vq8f9R0zXSmJwQNZKE+f810V9fS/dSp5zznnO95Py8rJOQdcTAGP48HPIdF+jD0Q89EROsQkUbsrc/7NvhD1+BaOpuXXlTRwjL9iQtxutroeuIuzI1rQ82XkA8su9QhH+gkSym56KKLuOeee/7p/T399NPB38ceeyy/+MUv/qXz25MpEiepeYjEMLRxs3DW3AzRNEIpUnrzG0i7HaEPIzJ2LlLWoKDhuf3RITWRhtKeobLuupJibMROKQWHhJQpEifZOJotP55N85nfoeHTl9G27GuoqQZavjAL/agrEFoU6ZSQkTSeZaPoSYyxF8O46XhCRVg9FbW8+gkL0LQM1pvfDO6h1JhvkTVD8as9EU0VpCLtlF7r/73SH59Fx/8s8VPApyyi4EZ22HvbcSVZdRiJiUtQpIMQgrYHrg1KGGCw8zyUvRpKPM3R09VTLo0YadX09y09nGQakToLe/2P/PRNow4RqcfUGokJJbgOVRUkUy6df1hO46kzcNYuqqgh73j0DtxsJy3TllW9Rk9oZEOV9fcN0/G/Z9dy0IWKpwnwwC12o0ZTeBLiqqTWcPEkuAVBtiNJ/UGdxCLbsLwx6E6GfAlGRQV5I4Lb10LNlgkA8oWNJFKhQx6yZ5E3DVJGfeXzWK9BO+xcHAH6YZMxNy3sn7PuNx/GfAqkpOf/flSh1dGxfAYNp19VUX60O1tYhoTsTt7Ttme6ru+SVmj7AqqqoEn/oY0CjpC4favs0DcpVFPEvC46HrmdhlOnYYw8EHPDTKTdDoC02zE3zCWy/y3IfAeqFqF1+gPkbGOPUlkHdjhhDtl3+VfaR7muxNOiuNlOMk99n/THv4qTafMf3D+5mYZTv4KaNFBrx1CUKWLqJqyVA7oPjJ+D/coPKurYsDZjbbqt4h6yNnyTxH530JOPvmffQ8g7U80mJqKFYPEE/N/L7riZ1vNuwyoqQ46nauOup+j/vkm1iJvtrNh++5T0oexV1R7jq69HnbhkUFS9ZerdaLIHke/GHBA90o/9JgybjBRRUJI4toLi9iKN+orjF4006VOmsu3JpTR9bgFevhM310PHo3cEiwnC65+oOq4MrnHgOYe895RT1l2nzyFXJHh+hFxL1OJ5HnFFUhtxKckompsn2xNF8QpoqknOTKN5eYq2Rk0ENg/Ytyv8aLlZfItE6vjdcHUhIUPjuJIs9SSSMX8xMKpha7W8kavhiKMvpbT+8so565uzMZquQBClbtLn8Ao9ZFf/3N9Xpg01ma7Yf1jeGPJBJRz17wOqqiDzLr+7Zy35rhKJ+ignTRuLmlArnHLL8ohqEdxsJ5vvm8GoK+8NDFsZabeD1YUSrcNcOYvIpMXEcJBeWCMbsnvZFe3uTCVBy5RFZJ5cCrJf+MXcuJbN983w9zl1GYZRwFo9Zzul13no46ZjdawM9if0WNV7SIhwBX53MpRNVGJO1d/LQ5Jzq6djv9O421FKuqqywwWkoXqMK9IhNyCqLrUIhubgZTdh/f22Sgf+uW8hDrkSJ19ky/3XBucw7NxbUCPDg+NZlocbG0Hd6dfgeS5ty2cPisCHE9U9g4Ep67pQg1HpFbsx4nV4nkdC9ag1HLJuhIjMkAUM6ae0W3YSgUfe0dE1gZAW5emYp/gOuWu3ERKyJ7L9YmA0anOIngEpqj9v9RjWqpvQj7qC+lPOCRxyLd2KmmqsbGEZljeGfEAJn+7vA5oUwcQTIN9V4k/3rOXjVx7N9rItJSVBy3lLcLs2oSQbEF3DKgyc0IchS92IeI0/6XNttiy9ZNDkbtA5VKmDLKc37ui9kJCdZXuhq2q9RSOGQkLLIaSDFBp5J4lp+ZNbVRUYubfJvvA46ZPPo/PXS2g++0a8fDeKEcOziqj1IygQp0ZmqzpKwqjsvCDton/PbH8PydD07U6Gsomf/sYh7/r3eqdxN1RKOkCsuInuJ5eSOu501GSaZKqRopHG6huTQ/UY94RWkW4ey22CmrifwlllXEYaR/LWd6dVnGOgmj4gMu9nSsWJiwKtF9yJvW0jmae+H6Tr78xEVVMFSaOIig1CwcEgbxqhTd+FVETIFRUp/e/WK/agNgwHINEXIe+1I0RkF5AiZmzD9aKYlgZYZB0dAMMrYSt+WytDT5KzJELZ8r5fV0jIu0VVBXrv27Qtn8GIS5ZUt9+m32lCaFGEppIa/znqTzkXRdOQis6Iy+5HuCV/TuAmcS1vB0cMCdk32aWz0ocffrjif8uyBr22oz7k+ywewcSzTL6r5Kdq9qGqCpGYjprdDLaJmkgBOpH9bsB88/r+epwRM7FffhR9/8n+xNB1h5zclRmqDjKrDgMY8r1wAhfyblCkU13oyrNQ1bi/8CM3Ya2YE6TzJibMA2MEpuUFjlXD6VfR8cNrUVMNYFts+9mC/rTg8+4gGbFQhKjqKIlIbfC6iLWA0Yreeh12243BPWSM+RbZUih+tVsZwiZaxTiRMd/C2vDNit/LsRVqRG/1KPYOBNa2T2XPi5q+z0qSapHuJ5dSO+mLbH34BtRUA42nTSXZBF48SqHgks/bpMbPx149e0jxtPK4HTHtDnBKVcelRAm6BvilF7W4uR4UBQauypaj/R0DU+GnLAyE7N4p00RTBSnFt+f2AAHElFFPlvrQpu8iSn0OueM46KqK1/cs94rdqLFDAdBVhZqIS4+lodrd6HGDVKodW7bgOTZZ26bb8adghjTJ4zvkSSPC5jwkEp2DDxwSsocRoxDYq95nn6L2w/Mx35xdMWe1/rbMt4NOCRHXaTjlS1irr60QabXX3YssdZGcMB9Hq8GTItQeCvlAsUsd8sceeyxQWZdSMm7cOB577LHgfSHEB9MhVyBRH62YgCbqo37TOc93xkU0QjHXRSKfQUvGUJBYK69CNB1D9NCFgcq6/fKj6Ad+AfsfP8EYP5fOpx4E/AmoJm1Shjpo4jZUHWRi4hKAId+rrE8MCRkaVVWQqlFV6Kpt/Utotc20jmoNnHHoSzNfNYfEpNswrThqn0OvxmtwMm00nH4VW/53TrA/NdWAKkoohW6s9Q8PVq4+ZibWG78iMnERbjGH29uJ3dVF569vp/6TF6HX1+KWijhOOnRMdjdD2ERXQtYcRu3Bd+E6JhINJ5uj476vDFkGUW5btv24E5pOPLeZjuVXV6j6UtNMwdZRPJvUcacHznjL5Etx1i7CXOuPJ+Wwq7jv65eSamjly3OXoOsaHoPF08oLAl1PPUj9J6YMUiDWJ9yA7erEj/wYjaeehbN2UbD4qbXMR1Pjwf6qRfs7ls+keeqyCps+lFZDNVtvPb8A/agrSCRjoU3fRVh9DrntOBiKikQipedHyKNJPEAVUGs4vG5qNFk5EvUaiVSGXusAQNJeKNLpCECie2ag1B43DNq64YhE7+66vJCQnaZs/yKjx5I67BjsNQ8SOWgGIt6Cl9uE9bdlUMpgHHMNMlKH5yrY25ebPb/ALzdbNRtr1WwYM40tj97yrkveQkL2ZnapQ37PPfdw991389prr3HEEUdw0UUXYRjGrjzEXokjJCdNG8uftquXdIRvZPSYwcpNRSbW2ujpBii1BXWIcuPjWNkNfiunxP4YR05HCgX9sAvpfPKBiloc6TnQ2048EqegNQVGbEd1kMAO3wsJ2Rn0mMEL2yRHnL2IzA/7o3vJz13HA9++iuy2dq7/6Yqq6bxCuhiGAq5ES7fiFnqD3swDnazGz1yKGo1jPjPPXziyMv3K1bFGpBTooz7FtseXUfzHalqmLKTzZzdhblxL270zgv2MnPVzILRLu5Md2UTXlSh6PZ3dWZJqkS33XbbDMogicVrOuYWOB/sd76bPX4+7bSNbfjJ3kKpv6/nfJS49pJ5ATaZxMm00T746UDYHf1xqL9/KtMU/JNuxhfZX15E+4ENYRNk+s6K8IFC2xfWfOJ/IpMUgPaTQ6DXjSKDpP67C/vOM7RakZlcsfg6M9g+MpquGh2YJHFfusGZ+KFsvtGho03chJcdDUwS242JoGlKCV+oF6aFEEnj4AYjaiEumR8UtFNDq/P7jJdOvEd+UL7LF6ntG2yXws9dJGBH+kYfxSm53XFpIyLuibP8aTv1KYEPN9pVBC9LIcd8EFCQSJ9uLHleqzwP0VPC3lqytautDQvZldqlDfsMNN7B27Vo+8pGP8Nvf/paenh6uv/76XXmIvRLX9VATKh+/8ujqKutCULAlmqr6YlNGyneK04ejHXwmQk8h7SyeUPGKJm4xC45N8R+rAYIJaOevl1BzwmTIZ4g1JwIjtqM6yPLfQ70XErJTCEFbzqMUHc6EC+4h276BbPc2Hvj2VWz4+18AkEKtns4rVGIU8PLdNH9hDt1/eoimz1+PZxWDyGdk9Fj02iTS6h30MEe6SNfBJYbrxaj9+AWkTr4IoapV1bWlolWUi4S8/7yjTezjnfp9+/uSuPX70zL1XmRvO24uQ9cTd9HwqUurftbNZ/BKOdTWwwNBITVZW9WR1Zw8ueWXoqVbMUYsxI0nKqI1mipQDcHIS+7G3rqRbY8vZdPdF9H0+evpeuIumr70HRyvb3uj+kRUof9aypPbgRF7p9iBO6CUKLqDmvmhbL10SnjR0KbvKixXEtEEtuuQiPiLe16hBwDFSPRt5deQ95gaN7/8Elcc5LclzXbVosV6eb03R7vp4nmK37K0T5dVUxS2FhXimkURCxkuHobswZRFMzXdw355+2ezDdLDy76O89pDKAdegHSM6jbKzgZ/uzn/XgpboIV8kNilT+g//vGPPPLIIzQ3NzNlyhTOPvvs0CHvw3W9/lLB7Z0BKYnrAgeBLlQUPYnSMhH9wDMqU3InzEcYUSKxRtxiLyOmLsEpmSiqwZaH52NuXEvdR84CqDBieStGavwN2Kuvr1IHCakJN2Cvur4ixXJgjWRIyDvSN4alFPSUHJZefSZdmzcEb9cPH0PR1IhNmIe1as6AMT2PvJMk5vYEdeN1HzsXJZpESdUzcvpSJBJFUTBXXOlHxGMtiGg9+uHnD7o/8rSC8Ms0VExGXHQnW39+O4UXf+9HEr8wBxR1N35RIWV2aBPLLw+Rjr79gqGUEtd1aL/7gv7992VaDPxs/MiPodfUIuobEaqNpxmMvOR7OD0dVSeJbm8X0Of0/mAmrdPuJacmh+xR3nrWPOysybZf3IKb7dzuPJXqugeifzyWJ7eq4uCs+VbVUiJZGnqRIm/VDLLnxjHXII168qFN32WUHI+IqtDtuehanzp60VdQx/Cfq7rioiuSHkvFEo2Ma+0m31vHn18vctKRsD6bRdVc8gUXYVRGw7tNHbAQbidSbX0/Ly0k5F1RFs2si+Rw+uybkj588PP5mGuQ8WF43VvQT1wIZgYlWgdSglCxXv2hX8YzdgYdj95BZPRYGk+bihaBWkqh2HDIPs8ubV5dKBRobm4GoLW1lVwuTLnaGeyixYThMUxX0P3HnyIVA2PstMCYQX96o6qCufIq7JUXYz/7dfS4ipKIY7Qc4E9UrSJIcAdM8hxXkpXDUCcuwTj5h6gTl5CVA0TbhIF+1BVEJi1GP+oKEOGKfMi7wy5anDgiyglNBVTV4uof/p5xJ/8H4Dvj593+M1w1hlRTRE5cQPTkB4icuACppnBdiZQyaG/WsXwm2x67GVFqx37xTkSpHVnahix24Lz2EMYxM9EOOafq/ZE0iqREO+7KyzCf+jL2qmto/syFjLj0QRpOv4rOx7+LcMzd+VWFvAuC6Evad0oq2+L0k80VeHVTZ7AdQPfvH6Dl7AXBa/EjP0bz6VOxn/065u/OwVxxJaLwNvZLd6PWNvgLnrEWgGBi2Pnk/cH+nEwbXrGbGq2HdCxHTSQfLHJCufXeHLSaWtRkw6DzdG3QPnRNxTGMY65BukU01ddecV2JWzMSLVVbPZounWCRYiDlRQrHlWS9YWgTlxA95QdEJi3GjY8m64aCbrsS0/EwNAXbczD0Sodc6H6EPB71f9MeU2F06kAOarDo2TaMF94q4UrJ6705UjGFXM5CcfIV+++2/fx1xd36fl1SSMg/jetKPATGMTN923nwmYOfz88vQOQ34b54I0gHISTmylmUnv4K5soZ6AdMRp94G9ue/BEALWdcjlx/B+ZTX8ZdeRkp0R7YyZCQfZFdGiF3XZc///nPQQsQx3Eq/gc48cQTd+Uh9wlc10MtmcSSdajHfAqnazNaMl51QibNnkFOiH7UFTSc/EXsk84C2wQ9Rk5UivdsbneyLQAAIABJREFU3zeyHCnxRYCuGRS1CUXdQt4NAknc3IC96nqSfSviX77+Jt665Ebe3tpNrHl/kloOSp14VjfOaw/hZdYFYw1brYhmNn7mUoQ0McZOw1w5K4iMe5l12OuWYRwzq+r9oWJjbu8k/XUejJlGx/KZYS/nvYyhWpZtL/Jjuy5zfvR/LLvw2xR++g1fHDDVgJ6uZ+Sl30cgQbp4ubcR0XrflhY7sJ5fiHH0DGShHVL7YUy6DSk9BIItjyzE3Lg2OEb8yI+hR8DqqwOPnLSk6hj0it00fG4mOZmsOE9PCqQl0I+6wm+PZmcDZeGB9jYqcni5TUOWEhWJDdlXHSSOK+kuRoHB9j5k12A5HoYisDy/hhzAK5Qj5AmQUJPyneqeEjSl64jHCrzZ1YQw4/zP+pfZWjIZWZcil+ul0a10yHN2BMiDG7Y+C9lLkGCvfwR93HSU5JjqdeKxZv9ZbnVjPbdg0FzWmLiYhs/ORNUF1sorQrHhkA8Uu3Rm2tDQwNe//vXg/7q6uor/hRA89dRTu/KQ+wyu61HImajR4STVAkJ1qtfZWN0Vn/N7L6ewnp2LMfFW2h74Fm62k6ap3ye/XRSpGgo7FnwLCdkZqqk789xcRo5fiGEYpJUtWCv6W0cZx8zEXrcML7MODQdHCJo+f32geK3XprBWz8E49tqKyLj1/EK8zDq8fHvV+wNRvU5XS9YOclxC9g7K/b77GfzbaapCczoFmkHjf1yDmqxHjyvYa7+LfuAZmNup8ZfHXnmSaD0zq6L0IbPi19RO/CLW5lf7heI+dznWqv7FS2l1D5nmrhiNuF7leRaJU1vbgPm7cwadv+qZqKrfM11IB/vVBwd3EZgwH9v1k9p2ZpEi5L2j5PY55NJF3y5CjhYD26Um4UfzugqCUUk/W7CQa6RFjfC9V15HAcYOayDX8QbDKeLrrfuYnj/eFXfb+3hVISH/PHk3SfLQc7BWzwkW0ActKOY2YK+5k8iJN1d9Trs9bbQtn83IS+6u/hzHpjZGmL4esk+ySx3yp59+elfu7gOJ60pcwLNNjAnzsVYNcGLGz8V+ZXnF9n7v5TT6uOlICCI6qnQH73w7NFWgIENRt5B/maHUnbXCVlq8ItaqO7ZLX1uIPm469po7AQ8hdHpW/JiG068iOvowZGEjxrHXIiJ1KC0T0cachtQSRE68CekUIdLot5NaVamL4FBdMEatbaV56rLQcdkL0FSBLHWRjpp4QtupyVdU1bn1gk+RWTaV3kwbw89bhPXSPX4rnUGpkwuDFjsi1oKX3zxY+fygaXQ+cRetX70dt9iLm8sgS9mKcTVwkag8BrWxM9j25I+oO/2aQeeoqgKU6qJrTs8WYgmDHDGk0JClLux1y/yJrZ5COiXsgg3FV6htGo2rRMhb8X7BuH9hgWmoFmohQ2M6HhoSB6+ihlyNJpHCby4fj/vfYVdBMD7pi0vm83UcYCQYocc4dWQLTTVxsustIsJEQeD2/Y66HiNnQ8zr2i3XFxLybrEsj5wxgtSkxUhklfnrHOw3fu5nEeU3V1/MzPXgZNqwt26sPi/Nvo675s5A4DJ0ykP2JXZpDXnIrsFSE+iqjf3yA+jjphM5aQmRibdgd6xCP/CMQTWI1l+/hb3mToSTIzX+czudlpswilhr7wrqfoJ9TpjfJwAUErJzlNWdB1J+wCrRWPX0NaPOd2jW3o2ietRO+iKlDX9HeFnsv9+GueJKrDd/g37YOdhr7sT6w8WYz1wLQsF1BZ6oxZi4mOgpP0CbdAdZOYy8aaCPv6FiPOvjb6DXSpJzY6GjsYdTFkor/WE61tNn73TtoOtKIorX37M+WRu00hmqxY5v6+bivPrgoPfVmnrMjWvZ8vB8pGPR+ctbcUv5ijHuZdZhr3+EyKTFaBNugzHT6Hn+TzR85koUaZNUi74T3kfMy2JnOjCOqVZHLoOspLybxBg/D1nq8vvyPncTUkmhqpZfU/m7cwZ9L6oqSKpFakTvoONuz8BtU1qRuLOVLUvP5+2F/86WpecTK27a4edDwHQkiucveuuG/6x1C91o8bqgU0DEsAHYllc4INWB7UZwPAXdiXP7h8fy2RGNJIt56qNRNMUj2vkWeP5nE4bB1gLgZt7/iwsJ+SexLA8HA+xe7LeeIjLxZiInLfEX31/5AfqoU1HSh+O8+iDGhHmVdnD8PESimZYpC+l9/slBmh7GMTNxXnuoP33dKO7OSw0J2eWEYdA9hIFRCk3VsP7iK1FbHSuBPoN07DXguUROvAkUA6+4Dful7+Fl1gFgrZpN/akLcD86pS9dfcfOhyIdnI6V/f2c+9qrSb0WpxA6LiE7TzUlf+O4OXT+7n9JjfvooNVu5YD/QsQawXMwjrwQTyiY7a9T/29fRJa2oY+bjvPaQ2iNYwNVdihHMOdgTFzM23dfiJpqIH3KBeiNo4mrHnkZJyuHkZi4JEjnzYbpbXsN1UofdrZ20BP9iuxuridopVNV2TzWQmTSYjxAliqjkCLWgpJoQEu3Ym5cS8+KHzP84mW4pRzauFk4a27uz8o47FyyTgojoqC0DCOZHoO99U0UI4ZnFYnXj6SgNfoLQQL0mjrsv91cYW/tdfeiHDwNt28RtRxpSky8DQUHc9NruIUc8tXFVb+XnBUbsi+56PtOg9R2N46Re7ty2y/MQU014GTawt6/O4npeBhOn0Ou+wKqXrEHPV6L1+dUx3UbtyToMVUOTm2i12pA1Qu45nDwBDX/WEds6xaijUkA6lY8SkyvpfeYj5EwInQUJKOdMEIesnchpIu1ai76uOmYKyu1Xqze9UFmnHRMIpMWg/SQCKyt7Qg9QnTEAcRGHwSqgTrpTjRZwsu+HpQZQVhWGbJvEjrkewCqKogVN9H95FJSx52ONubg6lGdaCPmypkVtZDbb+MVu1GiTWieAurgvr4DKUc1vcw6rFWzgb703olLdv1FhuzTOK4kb7SS+PAivHwnbm8XW351L7UTv0hxw9+pPX4u1rNz/QfpAf+FPupkzD9d2e/YfPhGasYeh7nCf01pmYhx7LWAqC6cVcgQPXA8tR/+Tzp+eO0gRyRnxfoWuCyiiqSohmm4ewNDlT7szOSrrMi+Zbmvjt5yxiyc/BsYVVrt2cRwHB2VwqDUSn3CDTgySuuFd4NQ8VQDpE3Hf1+Ommqg4dRpaMlavFIRT9RhuHkUz0aoBl62k20/W1Dh7MYbUxSI4OV78EqdQeS7jIi1oCYakK4kqRYpEseyPFw1QZQ8Wx+9hdYp84f8XmIU6H5qKc2Tr0ZN1uLmeuh+ainpf/86mpepaMuWnDCfLU8trexh/pN5NJx+FR3LZwavhZPdHWO6HinHj4BrZYe8kEFraA1EbJOaTd410FTJATVbebPnSBSjCAgibduIbd2Cs9/+/HrFnzn7kIOxj5qEsm4N6T8+xlEjj2RrAaQbOuQhexdCOjvMTlJSY4hMvBnXsuj46UIaPzsTp+ttulc8ROOpZ2GvvqFCN8PRG3DX3BmWVYbs84Qjeg8gRoHuJ5dSO+mL9Kz8MbHRl1cXrJKeL3JlZ3Fee6iiFrK8jdvbhWrU88JPXuPIz+yPmlCHdMp33J88dF5C3h2Gm2fT0osqeiRbm1+lderdWD1b0ScsQCggNCNwxqFPAK7QgfX32/p7mB54RoW6+qBas0KW9CkX4GXepHXKfNxcD51P3s+W5TNoufh+ZG5b1Yhh6JTv2ZQXCf+ZyVdZkb3l4vvRVRvwUGuHY61/tDIi/fKD6OMuZd3b3Vx0+3Ka61J86yvXcejIBlQ1AtJFyW7C7e2i+y+/oO6UqchEXRBB3nzfjOCYo2c9Rvcvb8XNddJ67k0QN2iefDWdT97vp7z/ZB7Dpy4lKWw8IdEaRqI034yX34zz6oPIUpcvIvfHh+n5w/JgrFrJkbzV2c1tP36CGybPxysVh/xeVMWl8dSzcNYuChzvxlNnIDQPe8V2HQdWzabuhGkUXvx9sB8n04Yarwn+DzsRvDOm4yH6IuRqIOrWgxo7BAChaSREkZwX4aCmErri0V5Io0ZKCBxSb72KW5em1NKA6fv1OE2jyCfS1Pztz0zatI4V6QiK19PXpzksIQjZOwhs+BDZSQBebjMi0kj6M1egGAI1btD02UuxV183WHn9xFtRqujFhPPUkH2N8Km7B6B4NqnjTmfrwzfQPPlq7JeWVlHYnYf14lK8jpUVSsHCqAP6a7+zL/6VZMsBTDqrgWIui1DSFIbQd3NcSVYN03tDdg2KZ1c44wBqqgGvmGPLj2cHzvGor31v0Mq50KLBawN7mFYTzjImzEfqtQi7B3f9HYET0jJ5Bh2P3oHqWWzuc8aBMA13L+JfXSQUgOZlsP4yoHRigKp6sN3YS7jo9uW8vSXD21syfPrrD3DaCUdyz8UfqYiWN37yGlyrhKa5jJi6hG2PLw2EM7V0K053O+mTv4pqOFgr+zM+Ws+cg+uAm2kHRdD584V+9GfVvEHjOPN/D9HzB1+sszxWm6Z+nwtu+m/e3pJhSybLjed+gkOOn4fz7JzKSH4hh6Yr2GsXVUxknbWLiEy6tbpScU19xWtauhXPKgZ/t5y3BMUQpGVup0X1PmiYjge2n0Wg6ypSenjFHrRYChew4zq620vOi3BEawGAt3NpDoj0UKetQ3UtzFGH4LoOluN/t4p0QNPJHnkcyRW/5QjTQUFBUETuRMeUkJB/ll0p7Fi24c4r9w9+dh9zja+JUerCOOYa9PhwrBWX77CNpJvbihcfgRrOU0P2cUKHfDeiqgp6zADLRE2m/UhFshbn5cq6bhGpw3rxXry+evJAKfioKxCxRiKTFiOdEuhJaj70EcyVfp9cNdaCMeEGrB2oUQ7Vnzwk5N2gqgqokYpe4gDpUy6gY/lMnEwbkdFjafzMpaCog1bOpVMKXhuY6lbuO66Pm46SGgNCx1rzXbQxp2ENSGMrOyGNp10KnjtoYSBMw907KC8S1n30TlzbrDr50lRRURedt2JIoOTapHQb+5ntosJVMok8ofL2lkrBrGmfPjpwxsufdf62AP2oKyg99WV/0eeMWXQ8cjtutpPm/5pH52+W0HjaVJx123UReHYe+rjpuBvuQWmaS/qjX8TZzmm2Vs1GHHgp8UNPpPTm84Gj72TaEJ7D21syHHvIaL7xhZOIaZJX2xwOnrgEDRuEgodK5uc30fj/zqs6kZWuXb3jQLIpuE+1dCstU26BZAMjZ/0cqUXQZAZ75WXBQleoaFyJlJKS4+H1OeSqriLNHEgXYfj14E7cwHB7cYhyeGuBvK2xIVvHwZEOmrS/4qpx7EQUHAcPBc+TqNICVGQ0zqZhoznUfAsAxcvgqqFDHvLeUC6ZfDcZZTty4Ms2vPaoS/GsbJ/mkY6XfQt73b39mkfPL6hYNByqjaRnllCMEj1WzYAzCG1RyL5HqLK+m1BVBRGNsHJTibXZGGqqES3dGogRleu6zRVXIq1s4IyXkcUOlMRwpJRYz92EiNRhv7IcaWa2m/SFapQh7y3lsfx8b5T02YvQ0q2AH21T60cFznjLF2ahJ1Q8x8IYP7dCQZV4C8Zx36xIdSvjZdZhr7kTL7sBaXX7WSJD1KfpTSNB0YJzKBOm4e49OK5EROvJlJL0FKODnPGUaMddeVm/CrvSTqaQ54xv3ElHZ2f1usXkaCKTFmOccCP6h29kW5fGyOZ0xXbD6xPVtTu0aPC3s+ZmWs+9kab//Cadv1mCuXHt0F0E+sao9exc9HRL9fOKxtj68A3Ufezc/mtMtyIVzY/Yn/dRRr/0I1rUAgcPT6M63Zgrr6b05FnYKy6j8dSzBqm/g39P2V3taB+qVHTXxs3CLpq0XHw/I2f9nOapyyjEhpO1DHq9FIrqBamh5XMMFY0rsT2JJwki5Kqi4Bb8xR3F8B1nJRnHcHpARjhiWIHXOuvI2hqqUqRWe4Vc9GBcpy/CHjEoFF1UrxQcI9s6hlKfsKoI68hD3iXvputCjELgjEN/lk6MwpD7jhU37bAzg+NKLJIoWgxpZUFKrL9cV5GlJIsdIPvLKcvZcNt3oECNIEToqoTs+4SjfDehxwxWbS5SsCUJxeKHz7xOy5SF5Nf/DeP4Ob6o1YT5RE5agog2orRMrPi8iLUgzU6EUIiceBP2G48htz2PtLortpPFDjRsDCP8qUPeG8pjuS3n8ZwznNh536dpxs9pvfg+nL6oecOpX0F4WaznF0B+I/bbvyNy4k1ET36gbwU9Am6JyMRFiJqDBjnsxvg5OBseR+gpjI/ejYjUBe8r6cODewUJqCpNn7++YmGgecoiimHa515PwigG6ezQ5zCuup58b4ef3t1TrOqcevk2zBVXYv/9NjzL4o3/a+d7l08JnPKRzWnqamqrflZE6nxnfsJ8RLQePButfjgNn7qU1gu/h5pqJHLSEowJ81HShwefk3Y2OMdyVsj2+1biaVqnzCc66iAio8f2RawXoegGSy75d+oTkubJVxBpbMHLbRkcwV+7CC09HP3or1e2+jv6Orb96i7srINy+Ewi//bfGB++Gdf0yDx+B9J16fVS5NwYAqiNlUhHc2jY1RcOdpBd8m4m//sCpuM7EV6fqJuuaHiFHgBEn0MeS9agezkcqXHYsCKvba0h62gY+R4U4ZKV+wX7M6IRerMmmpsLXovFE/yp0Pc9Wu3vw1WF7CtIz3tHh3kg1UrNdpRRtjMOvKYKdGsz5soZmH+6DJBV7V850q2kD0c7/IK+7he3EP3EjzBOWoL91m8x0o2ohqQ2VkJTxQfO3oR8cAhDRrsLIYiocExNkaTi0NOS4O/dguMmfhr7rafQD52CtXpuf+3N+LnY0F9Dftx1SL0GKTSsp7/arx781tOVh4m14OXeIhnJkTNGYFlDq66HfDBRVYGT7aRGlHaqfmz7dDVPRCnY/vaZkmRFya/TPnX/OB4mLectQY9qYHUFdeH64edjPnNtoKiuH3Yu1guL+utjT/gOxnHfAEBEarHe+JV/T7zxC78V2obH/XvileXoB54xqMa88MoztE67F+l5frunvmtSVUHJtXFcD01ViKp6KPS2FzGUCnttXAfghh/+kWWXXkdy3Y0VdYv2unuDbd3n5nD85Dsw3Tg//fZ0bMdF11TUSAxtwnzsATXkxvi5QblQ0DPc6kFVDbqf+xXpSZ/F+vOsynr19Y+gH3gG9rplQJ9zrhhoH7oG528LKm36K98P9t169nysrEXmyaWkP3kx0WQMGTeQ+Q04Gx5HP3QKdrS+stSj2AGqBpGUX8KkRf3ypUgNaqoBJZVG02ysv1zbXxd/6gw8BXD7Mw7KSuzGCTdWTRuVQ2SX/DPprns7pbJDbvsOuaqoWEV/IVwYCQAaUzFEViI0h0TEY11HgpxmEMn24KHQa40hxQYAjKhBb0+J4U4vijEMT0qSus6vciqfB+TWFyD5iff/QkP2Stx8pqrDPJSGiqfog0rNdpRRtjMOfMIoYq3sXzy0Xv1R1W4XHgKlZSLaIWcjXDMot/Tfn4t24BfwSl1YK3yh49SHFyCEjpvbWiG6aSVHhnPbkL2e0CHfTSjS4zh9M7nfLMU77nSOakpjNNZir/0uxpFTMZ+5pjL1fPVcIifehDzoC/6EKzYMZ/0jGAf8B9GTH0AWt2C//CDG2Isx234/SNBIlrpITLwNK4wShvShqoK4KKC4JcxNG8k89X3cbCctUxYh4nV4UgRR5bIDLlUd7Dwd913WX4N6zi0cnB5Bs5IjgoOJxutmwlcHBvRYFKEIvL468YF14cKoQ0QbMFdcVRn1/MvXiUxchBQCz8qiNY7FfmU52pjTEHoKr2MlNmAc9bVBiu3WqtnU/9ttFLwkxVL5IS0xDAVVZinme+joynPPr1/gii9+kuF1tfus87C3M3jxp7oKe0/Bd46ee3Uj59/xO771les4fFQTqiKw/nrDoFRJgc2bnZlAOO20E47kros+gvPKAwPGZSPWmu9WaHfY6x/GOHIq0uql8VNTgzZ95fet5xcSmXQr1po78TLr/DTxsTNweroQWi3GpNvBs0BK7JfuqdQFWTUbfcICIqMOR496lZPTY2b64/+Ii8DuCRTjnQ2Pg12qUCcufyfNZywGCMTmysdx1i5Cn3gb2IP7vjuvPugLLz0/YOHguOuQbhFN9Wv1B/4eQlXp+IAJKJZs36ZIu68PuaZQ6ktZx0jguh4tiQhkwYj5aegvbIyjjzEwst2UlGGYZn89rBE16O3tYX+nCxmLglPEUFWetTU8V+J1vQT7v7/XGLL3Ih3rXUW8B7aLrFhUI061Wu3tHfjI6LE0njYVLQK1lMhbsUELp95bj2MDkZMWg+eCULC2PI/R9CGMcV/Dy24IuqxA2R7OJTLxZjzXbysoovUIs6vCNjWeOoNtTy4l8akZqGoqfI6H7NWEDvluQi110/uU3+ps68M34GTaGDXjR+gHnoG0equmDSI0RKQOvG1IM4P3+v/CAZ/rc8bvw8usQ46b7ou8FTv8Fj8D1IUFQ8it7wKk55FUi7tEpTPkvacc2eoY8BBu+vz1dD1xFx3LZ9Bw+lV0/vJWX/BJ1ysc8OYvzEFNNQRtoDoevJpDLrybtnsvJtu3zdHn3ILjjCApN2M9MxsRrUcbe6kfrVz/sO9YG3WISC3Szva3OzvsPESsEYSCFBqysBlr5dXBeYuDvxSUZXgdK8G7qPq94llE5TZ0LYonBZaaIOG1Ya++nnSxg/pYCzd++Tqu+8ETzL9gMnpoCvc4qkVfW85bgr59C5wJN5DIpRjZnPbT1ruzKNEGsnaKpFFEliprcEWsBTyP2378RCDsNu3TR+Ou9iM6Vp+THDlpCV7HSj+d8uAzIdqEQAaLpUOpAkvPRTvkfOR+Z+L0bGXbkz8idezpZP/6Sxo+fRmoOprhVdUFkVaeugmfwlw5AxGt72/X5pTQDvkyQlGxnvtORZQJI0HkxJsBD+u1H+O99bifCdDTBopa/f7AwzAUNGycvntPO/hMhJ4Cz8GYtBiKHUinhBQ6zrqlJMZejtvTVfl7TFlEy5cX4HR30P37BzA3rt3nBRS3j5ArioZXdsj1OHmypDX/WZuI5eku6LyxRWH/ERK9kCUfOwQn179YYcT8lHXDy+KpSYTj1+vHdYNSyUZYW5COhdCM9/EqQ/ZWhGa8q4h3uV1k89RlgVDmjuZvAx14NdVAyxmX46y5GXNtvwikp9QPFm7d9jzIKchCO1JPotcdgLlyBsax11Z0WQm2L3YACopRS/Tk/8azi1jPzhu0uFh3wjS29vSg1UbD53jIXk1YWLybEG5/q7Oy4VQ0w0+97VObrNg+1oKXe5vS01/FemERAlBaJiLzbVgvLEI//HyUlokIBBIF67mbsFbN7nfGYy1I1PfkWlRVYHX8Y6drlkJ2P9XqwMriUuW+xE6mjY7lV+N2baqMgP1kXoUIlZNpw81uC8TbGk6/CmkViIlsUPPqZdbhrL0DqSXQDzsXe82dmH+6DHPlLIQW89PWj5iK9cIiSk9/FXPlLCi0QSRdUZcrIrU4bSv7a8ZF9dpcL78ZCu3Ing1sWXo+CdE9qPY4ue5Gpn36aBw3THXbE6k2RjvuuwxHpFEnLsE4+YeoE5eQ9YaRjid45NvTWfm9r/PIt6cHWQ95K+bXfg/UIzhuDk4uy6KvnMKxh4wGoLm2ijCb1e2Py8PPx15zJ5S2Yq0eMCEcwk5L12PT0svYuPhctjx6C+mPfxU93UDz5Cvwejfh9bbh5TZV/ayaqEOaGd8Z7ztuufZdMWoG1ZBbq+Yge1+n9PS5mM9ci77/v6OMOg0Ra8HN9eD2dlU/R6ETyW7E6niz4hrNFVdivbAIrG7sl5Zi/eU67Gfno405DRV78O+xfAZOdwedv7yV+k9eEtTB78sCimWH3LUdBKAKgVvsRo3VIBG4iSiG4y8a1iRzbOiqpViyabS2IgA7Uovn6niO/x35EXLTf3Ir/d9b0tDJWCpKxMPbvPb9vsyQvRQ1kaZ5yqJ3paHiupKcGwt0JXYUTBnowLeeexPOmpsHiUAKPIzx8yrt7oS5eK7p25cBtlTa2aDLykCCOe9TUzCfuRahKIjGYyq2kcUO1Jp62jL58Dkestez7z4193A8RQ9anZWRZj6osR3Uv3H8XOw3HuuP1ng2xrivYb3yYH+q5MSbcQq9eEYDxvh5WKsH1OuMn0fOTQK73mjFKND+wNUfqLTFvZ2h6sDUeI2v9l/oDV5TjFjV7cqUH/yR0WOp/+Ql/RkfV9xX4eR4mXVQ6hjUrsxaew/G2Euw1t7VHxG0s34kfb/Poh18JvaaO/0atMw/0EedGtwbZfHDgb2jy2Ua+hFT0dNpGk6/CoFXdQV+eH0CoYbrknsiQ41R4Zj0WKkBr/aVRqCh9605lvUCohRwScGYaei1TYhoii0/v53Ci79HS7dyz3nzmXbfH9jSU6R5u4iOs+FxvwSonDq+nbJ/NTutjZ1B94pHGT71ezi9W5GOgxaR2C/ciLmdLR/co3cmnp0HqxvtkHOC96Avem52vaMKvF/atAB35OlI20FN1SFGLAq0F/yslHocT6HjB7NQUw20TpmP9eftS6TmBa3iZLEDYdQBStXfQ6tpDBb0Gv/jGpRU05DprvsCZYfccWwMVUMCXiGDFq/DdV2UZIKI241UJfGoydbsCKSUjCxtBMCN+b+XYyUwtB4i0QjtWQsARcggjy2p62wuOTTHFZz1z6COPvb9vtSQvRChKO8q4v3P4LqSHDHSMjeECKTFll/cQ90J09CStWh1rXiFNuzn5w6ypc5rD6GN/Vr1UhnP9TM+7WxfSeY0rNyGIJtHOiXcWBP3Pvkb5l9wwC67vpCQ3UHokO8mLDVBrK/VWXmS45ZKvqiOFkVKD+PoGX7LByOF9eK96Ad/CaINSDsPuob0ujEO+xKWAG/j40g7j9vVjtqUwNabMCYuRuAi0ci2kMPEAAAgAElEQVS5yfdM9OLdqnSG7H6GFHKxikHq+sDXBlJ+LTJ6LA2fmYrRPAqERuPka+l4sL/nuBKt8dN6rW6c1x7ya2qNukEPcK9jJfKoy3whrPWPBOnsxthpeK6D0KK+foJTxGg4HGv9zyocdynUfkGrvjINP01ZgqrT+ctbaZ58ddXa49pULZYXCrvtibxbsaGBDEx3L5dfNH1+Dkq+h4ZTvkzdCZ+l88n7sX42m298YS73/PoF7po2H/fZ/oUddfRk3GL/hLPcjq/8v5dZh73+EX9sWlnQarB7M6Q/Mhkn24mIJtGTCRRcf5viNuyX78NaPRf9qCuw1y0LPiutbux1y9AOPtNfCDhy2uCJrmdjTPwOQo8hzV7sVx6CUiZQc4c+x12JIOjBefHGoJe4MWE+EoEstmO/9j/oh51Ly5fmsu1Xd+CZxeqOfqQeKGempCm1v13191BiNURGj8XcuBajaT88RScmCxTVfbNsKVBZd10MTUNKiVfoxkjU4boekVQNurMVzxAoSLIFX+jtQHsjrh6BSF+E3YxBvCeIkAOonlPhkHcUi6iNKvYbfyHy8env+7WG7J2UHeZ+3pv7cChND1AovPh7Ci/+HoAx1z4MkQaMSTciFBWEhjJsIl77Sj977s1foB/2VSITb+7rlGLguRYKEhCISBrt8PPwlBj64RdWOu4T5nPnJadjqwbFYhglD9l7CR3y3YQaifCWnWbElIV0LJ9J7KDxaMNGowgXcBGeh2c54FnYz9/s9yXvXY8+fg5CyWNuWIi02xH6MCKHzMO2ehF6DSIFQihkHvkWhZd+H4huafX1KAbYRQt3F6f2/CsT55DdQ5E4LVPvRjNchCqQrsSxVBRFo/OxhZgb1/bViPo15OXf1x9Pi1ASaVrPvQ5z4xxKr/WNwzHzUVMNfl3Z5EsDoamB6tNEG/oXnewszmsPgZFG4GH1qVMPjBrqH74J7Czmduqs0jEBD6EnwUghXQvpdiNiKbTDp4Be7+/Xsxlx0Z30PvdbUmNn4KwdoOQ+/gYskdrl90PIruHdig1pqiARLSKEg0Bh6y+X4mTa6P79A7ScfSN6UsdaPTdwUlsmz6Dj0Ts4av9hzL/gAIqqQXLiYtyeNtxcDx2P3uG36+ubcFaLiOuHfhnrzd+gjTgFFBVNL4GVQY0oYGiIYgfmwMnjsddiv7QUJTHCF2hTIggDpFDQDzsPtJi/EGXnUQ76L/T9T0MoKp5UEG4P5sYF/Xb/Q9cgZQLn77cH34GItSCExHrhO4OEDo2jZ2CtudO/F19+AG2/z9Iy+VKEkaw6qRZ6wj/n4+fgOAqKEaH1grvo/NVtwbOl6fPX0/nrJdR97Fw6f3kr1tY3af/vKyrU1vc1yqJuruf6EXIJXjGD3jIGKSWJVD2itBY3oiA9EJ7/HDzQ24SZqEPV8wDYVowYfSnrfRFyVdrBcZK6TnveQ9ElXuYtZLYDkapM6w0J2Z3krRip8TcE5WDl56rt6sSP+Bip405HaxqNiMcQ2HjmZqzNDyLtLiLj5mMDmBn0Uf8PCpuw/vGwr6OUHINwujEHdhqaMB+EwF7/cGUm3csPQNOn0aL1RGtGUSqFz/OQvZM9zmPKZDLMmjWLjRs3YhgGY8aMYf78+dTX1/PCCy8we/ZsTNNkxIgRLFy4kIaGht19yv8kgo29HrW1jbROvx/FcBFuhtKbs/snXPvNR9IAqTEYh34JEalBxOowN/0Aafu9SaXdjrlhDpGjb0daDqri4na9QfqTF5M+5Xyc7g4y/989qJ+axfO9cSYMj6GWzF3qhBSJM+zcW4K09XeaOIfsfjRVoOsFrPYH0ZvOQNGbMXSBFArNX5yFYCaeq5F3/HqygelvqgpO5h84hSXbjcPZNHzmMjCtwPGFAerTJ92GtHqx+9RUy22kRLwFaeXQxpzWn4reV5qhKCrmn/uc8fRh6IeeCcJExGuxnr0JShmMSbciNBOz/bb+e2fMPOw1t+O1+y2lasfPpef5PxIbM43IiENw0claMZywVcoey7sRG9JUQSrSjvXmN4Mx0PjpGXRkOzE3rgW3gLV68SBBoMbTLkUqOrqrYVkelqqwZfnsYHGx88n7aTljFs4af1HUbl/lq/+bGT+q/coP0A45h5JdR5ROUCVoIIxaZKEniOSUj2k9dxP6UVcgFQ0hXcwVV/bVi1/oa4M0HYN2+FcQRgTjoM8ipYPn5hFqDHPj/Mr7bdMCIgcsBsPvpR4sVrlO9Yh3rBn9mFmg6BhHXojnlHBWz/V7rFdJn5fCQBx4KbYTIbv6f+n9w3J/Qe7LC4JnSzmTxhh2IK3nfxenuz2IlpfLliD5noyP3UWQsi4dInp/hFyNjUMCNTWNqL3boAayZh0RLOoiBqNEhs7YoWi636/Z7WsPaUQj5PMWngRV9mcjJXWdtrwfL1eiAueNVehHffb9vdiQkB3guJKsOozExCWBjc5aMRQE6VMuIPf335I+YATm+sv7n82jZmK1L8N8czbRo28HV2JtXokxaiLG0RfjZd9CSC9o+wv9i4qRkxYPbnN6zExcGaftvpm0Tvs+pbCTUMheyh5XPCmE4IILLuCJJ57gF7/4BaNGjWLRokV4nsfMmTOZPXs2TzzxBMcffzyLFi3a3ae7Q1RVIaKoRFCJKCqqqqCqghqtiFHoYEIyw4oHbkZRtoG9GbPPGYe+Cdebs0EF/ZDPYXXeSWnD5ZTWX4HRNBklfkRwHGm3I6SN9cwM7L9cilx/B6rMgufS+ctbqZ30RRIRleMaShjOVmpiJQxj1/30risxWg6ieeoyRs76Oc1Tl+3TfWj3BRKxnO+Mt5wFWiMSF6QHXhGJg7XtZwg6SeglUkYvGB49moGpJpCOhRqNBWO1jLTbMZpHozeOqK6Y6pYGi1I9vwCkhyy2B+nsSvrwQGRKljoDZ9z40Pn+ffDmpZhvzsL40PkQTYOwMd++cdAilbbfJ/uPs3ouNUefwpZHb8G2NUpmAtVVgvsyZM9kZ8WGErEi1oZvVowBe8si6j/5FQAUXa86JvWm0RViR0XitEy5JdBFcLOduGoaY9Lt6CfeibH/v2OunIH5p8t80cyOldjPziGaAqEWsbbeRmnD5ZhvzkLEDH98bndMJTkCzEwQ6TGOvRbr+QV9zvhZIAtIqx2khwCEVgtSVr3fMLdhjJtO9OQH/PR3ow7MbUMLHbqWL6b4zLUIJCJaD57p907v69Chj5uO3b4KIQRG8xiEmSF11ClERo/FybSReXoZWjKJka6j8TOX0Pi5mbR9fzpvLfpPtv70W4G4275athSIunkehq7jOjZeqRc1WoOUkvqaFiJOBi3ikC3W4XkOx6YLqEKS0etQNAdFtbEt3yHXIzogKNoqupOlLIWa1DU29TnkaroWZ/0zu+FqQ0J2jONKeopRMqUkPcUojisx3DwdP5hF+t/OGDyvfWshevOZfa85WG0r0YePpbT+CoqvnI219TYgU9V24rmDtDWs5xei6FFfX8SzKz6z/Rzc0NVBc/Jq6IaCqRbIi15MtYC+C+fLISFDsceNsrq6Ok444YTg/6OPPprNmzezdu1aIpEIxx9/PABf+tKXePzxx3fXab4jqqog8y6/W/wCv5zzZ363+AUUyyNe2kz7Pefz1oLPse2+6XzqgunYHctRtIaqEy5FOJgb5lY1aGWEPgwv+3Zl9GfNzRgNTbR86XoKrz5DVGZIrL4M8buzsf/4NRKyfZc65UJRdlqlM2T3I6SD3vBJUFIIWcR8/RrMt25EWm0IoWI0TwatDkWzUWQOvfgmcTeLofeiJhTUVANCH1a5T30YbiGLEolXdQhQqjtFUjVAr0VE6n2RtmNmgWKgj5uOlF5favCZmJsWbhchXOhHzJ1c1XtHRGoqjiM0jeYpizCdRMV9KfNu6JTvxaiqgiKdqmNAq/XroL1SseqYdJXIdrZKUFJb/3/23jw+yup8/3+fZ52ZTJaZkJUAUUQF0bpTUWu1fqy1tXVt1QpudQUVlUWryObCooICKlJXrMvXVu1iq62t9lOldcMFFEVBlhCykEkyySzPen5/PMmEIaE/7ccFNdfr5SuSmTzLzHnOOfd9X/d1ET5+IaXnP0H4+IVk/VIyfgEiVATSRd97XE75H7qCbMXD2jynV1JIHz621zklKpglgWbCysXIbCDWpu95GqCClKBVgtBBaCBdUEJ9Pm8y24bMNOVU1kk3IkKlmKPnYYy6GSU2PFdFctc8tJ0A3Ay03cciM1tz12K9fAXuhmfRa47EevkKsn8fg796HqrmEP/++MBz+OgzcF6dgvv6Vcg181E1G7UwYKpt69bwdW1b6g7Ina4KuZduB0CYURxDIWJGiRpJhALJdBB0H1AS0NTXdvmPa0Ya1wpeE0KgmwYdaYnhJkAJlAmjuk5LV8FcrxqMs+ltpP/5WZf2ox+fFRTfCfYIqtL32qwWBvOZUNFrDu2zGNXX3An0vYdwrSCJKpScu09fe3A36fLmEx/m/k3Ko6m9BUdLY6kp0iKJb1hs6tjMSYsuZfQNp3PSokvZ1LEZxXT7A/N+fK7YqVdL3/d59NFHOeqoo9iyZQvV1dW51+LxOL7v09bWRklJySc6Xmnp50udKyvrUf7NJC3+suRtUoksAKlEFr+zlcbf9KiRq4WlqJpAKf0+Eh+hVyL0OHr56Qi1EOlnkX7flRGhBvccUNtnYb8+P/89mcbgvzXziR02p5eKrvPqdUQPX4zatWH9rO//y8CXef7P49yf53j17RRCLUGoOtmPpiH0OEbledjNT2GUnYjV9TO7aV4wJqsuIKLr+NYG7MaHQI1jDpmBtWFajoqml09EmCXYH/8O46BpOVuTnKhUHwIwSsVosNpwVtyEGDYGfc+xWP+6Oq/nVj/weoTad8AlzCKklQyCk21eF3ol0kr2/DtcgS90Ln3oZcb/+HuYRTqpRJZUIstLS1ZxzOQDCMfN//iZfdnje3vsbNfzWY/XT3p/maRF+xabcB9jQC2sYPDVf0AYIcSAG7BfuS43tsxRNyAKSimLKnnH+sttb+TmbYCafdo59LQo9qs9f9ut5O+3rg42isLrO6EaHZgb8z19kCrCz2J1VXq6xeK6xY4kOsJvJbu+59kydl1AaOitSCeB9NpwWp7DqByD8/ELaPGRQdJq1E0IJ4m1fPI255uOdNI4q38VBP7bCcAp0YGBsrubxth3IiISbGqtl6/oneA9eA6lR5/dqx3FXTmX0qMvpP6+iUCXC0M0RuVZt2KUDPhU3+UXiU87XrvvQTG2ogqwfJd42ET3g8/UKCjEiheClIRDwfhp7zRQhM/wSBK7Q/BKc4jDqlU0M4trR3KJQDNs0tHpMtBtRi/aG9VLEi8Is7UrIA9XV5B+4x2KshsJDdmnz+vaGdF/bZ8dPs14/bLvzU25lB47Ppjr+lqb/WxAXW96GqP8BMzBVyO9DpymR/HT7wVzZ2FN/ty53yT8bKJvvQsjQvmp0/BSbRSVmGjx0j734C/ds4r9TxnG5ndaSCWy/HPJKoaeVUarTHLVY3MoL4wz/4xryNoWM04Yz+K/PcKKDe9x3n3X8ehFt+BLn11Ka9C0/z50cl2Xxo4WHM9FVzUqCkv7PJ7rumxubcQR//l9/fj6YKf+dmfNmkUkEuHMM8/kr3/96//5eC0tnfj+51O5LSsrpLm5Z7NjouZt6kpriyiIabR2BePm4JFUnDIBmW1DiewS/G7o7UFFUKggREBXxCO8xzJ8pxGn4T789HtBZlErJDR0AdLPghrD3P+XIL2ckm/35ktmGsFJ9k0h9u28a/4s7/+Lxpd5/u3P/Vkthp/neI0VGyh6PBgzTgPGwHFYm+b1+in0OEbVhdhNTwQVdWMo5pBpQdVOqBhD7wTfBTeFdC0UQ0EbfAx4SczDZwMq0leRQkWRLsYB12K/cWPPIjvywlwAoYeKAl/l7XpujQOuRZgD+l7YnQzocYxdZoPTgFBCwTOhV+K+86vgfV2BUDKZoam1g4tuX8Z9F52NeB72+9EAwoUCTemgNeHg7oDZ8WWP7+3xWV7PzjheP8n9qaogTBpFQEFxCC00E3tjjwaHMeQGkumC4Dt1QVMr8nod2+0w7tZU3jG75+3S2sLc2DCjJvarV/aiSep7j8vZ8Un0vsenUDFH3woCpJQ4WxvQCgHh5Nn+GPtNAhQEgain1RWMq7Hj0MtPQwgPUEAbgNCK0KuvRCg2xm7HB1WmUTchjEKk3Yhx8NU5FXb71ekB0ySbwNhvCs7qpT3XF65AZrdiv3xlTs/BFxp+2+agr30b4ST3w0cRCqhFcdw+1hKzcpdc77gWq0ItrqRTRkm2pHbKsQqfbrxuew+tySyGEHRKF13XSLduBcATISiIovudKKaP6xp0pH186VPqd7LJjfJes4U/0kPT02Q6ynNaLkbIoL09w1BnK2k/gpbZiiFFLiD3IyFA0PLWPzC69gvbX9fOhv5r+3LG687wuRdqDk1PzGDgZUsxa2dgNyxDL/0+QitD6CVBoQkVvXww4CH0MlCL0asvx6m/PUg8WknM0XORVnug17H2SbQR52McPB371R6xN33fa5CYiMJSvORWFDtDprUZqUUZdWoloQJBNiV5+7mttKzvxCjoCXtSiSyDSqoozEZ5/uIHUFQBUqJqKjIKC874JfVtjcz9070kOtspKSiiJdWGYv/n5P2OoBsKG5N1/OL+qdQlGqiJV/Krc2YxuKgGZxs9m0/6vs8aX3Yi55uOnTYgnzNnDhs2bODuu+9GURSqqqqor6/PvZ5IJFAU5RNXx79oKKqgIB7KBeXf+tkedPiZnFp16dFn4239N8bQHyLdVqR0wKgFmUR6wd8IP0N20zaquoOvwW56AqPsROxNc/HT7wEQ3uPX2Kvuxm9c3uPfKHTcVYsAkHZb39YUnoeqin56+TcQjmuiKw6KEgQOQi3MUcm2/WkMmpgbc056A7peQfajbYUHZyCVQtBMhF+HtflhjMoxSFUBrzUIjrUKEEXgu8hweZ5FmbQ7cuNye59n6KKaG8XYby/G3HNSjrbeLdwmLQ/MUoS/FatuG1G32plo+1yJX3MyXmc7TgaSyy7j2lOnc/KNj1AQMzj854WId38ZVPnCFRQeNIsOtXKHQXk/dh5025q1PX8PxYf+jObfzEItLKX0h5dhlA2isxVwByDx6RaW7O517EEf37MCNfuUcuDxZm5sqIfd0WcQqkSHBEH5+w+hjbgEs3Zmjnop9ErMgZNw3r4TY6+LsVfelZufxSG3IFN1uTnZb10dWJ4dPBUFDzAwBk0EfTDCb8NaNzlvDXCsBHp4INl12wmAKgoiXBBU0FPPBZoLb9+LUrgLxv5TkEZJlx0guQDceW9p7n7sN+egH3oHSrQcdXtrof2mIBUTJRLrcy2RmQYqTryUrc8/Qsn3LqBTRr+260rW8TGEIOs7hEwDP705eMGIECkuxUltAhOydjGuE9iZhZ0sdcogmtqyNKTVLsp6CClBiCAgb2tNoVCA0kXzjxo6WQ9cX0PRLZRYDc761zFGn/0l3Xk/+vHJILzACjfxp3uI/+hCjMqzsNZP3Wa+mgVKBKym/D3ukOno1ZchPAvpCpzVS9B3PRElOgSt9njct+aCEQsCdTvYP7iugkIa7CzJlx8j/d4/iIw4gtj3foH158mkuoSGDzlpNm/93cRO9ehaFMRDKEJBa9fJZC1cy8Ms0Nn0XAu7HVPNRb++nsbOFhacfg225yCAtJ1FV10UIfAlRNWAudDpdeL6LmHdxPU9HM9FUzSiajQXRHd6nbkgG6Au0cAv7p/Kk+MXYm6jZbKj9z116UKMftG6ry12yoaI2267jVWrVrF48WIMwwBg5MiRZLNZXn/9dQAee+wxjj322C/zMvuEqiqEoiGIGBw15UAG7hNQ9iJlYTbaBZSPDQSD1Fgleu13seqXIJUChFGFIhxwGsBrA5nF2rRdT+LGmzEHjkfmZF+6qjCZZvShJ6HEhgcbqzduRrhp/NbVAIGv7cEzcj04IlyBNnIiTU/fRpj0F/wJ9WNnQGfKx5WF+ISDzbyfDcaS15H3U9HL0Eu/H1TNSw7BbngIY+A4QkMXYAwch92wDIGDokhEaA+M6nE4iRfAT5NdOwG7bgHCa0MID/vj36MgsV+5BuvlKwKBNyvR0xvWRd3dFt1iVH7Dcuy378UoHUeodmHADrEl7qrFKMLJUeeD4wQ9aIpis+mO82l66lbcljrc1i2MHDyAP8w4i8oBIN6dkd/G8dpUCox8z/V+7JwIk6Zp2UQKD/gRzb+Zhdu6BWvjKurvuozNCy/BNAxee3TNp9YHcIXk22dU54+NroTmthDhCvzODTlhNwUPL0UwPofcjlE6Dvvte/EbliOdDvzG5cGxMo1IKwFGceAwEK5AxPZEGz4GgUQKAyltkC6K8Hr3Vm68GaNweB/P4UMoWGTXXoq9eXGQtG15Cn34WGS6IQi87Q7MQ+djHvkA5qHzkVo4cDKIDUeJDcfYdyKKtFBUv7c6/Jtz8Du34nsC46DpeWtJd3+6u+oWyk6a/LUX9My6HpqUWNLFNHT8TBsAQo9QVFKO3f4e6JC2o4BE8T1M16alILCA+8cGHc3IIKWK7wSVNiNk0LK1EwCVIGCI6joAGVdH0IlSsTtew/tIu3/N7sfOjW4r3I7Xfo/fmcgF49C9Pk9F4GI3/zZ/Hmt8GEUvQToebv0/MEZegogOxPrXZOxXrsFvXY3fuDxg1VkJ7FeuwSiK4Wc7aHx4MoUH/AiAwgN+ROPDk3PtoW7rFlqfvJpRP63CCnkccNmeHHDRHhwxbh+cjMvrj63hb/Pf5PXH1uA5PnscOYjX7/uQKd87n7pEAxMevRlFBK7olz58Ayctuoy1zXVMffJ2mrPNJN0kW9qa2NzayPqt9Zy4sKf/fGOyLtd77vpuLsjuRl2iAdfPF7/c0fss1+nvY/8aY6erkH/44YcsWbKE2tpaTjvtNABqampYvHgxc+fOZdq0aXm2ZzsTVFVBhEyWb86QdiQRXTBq7AgOzDqgCaqLDd5urWb38+5FLfSx6xehV56DwMNaewWhXecElFsRQlEL+lbVlTZO85Pb9PuejMTAXfMI2rDTe1Ss1WChF+EK9N3H4FgqDLkQLVqc89i1Nq4i/sOrUFWl34v5Gwi7E956dhP7nTIILRTHrJ2J3fBQ0NvV/BTmoEmAilBLkE4DEiXY6Dc/FVilmbtgDLwUIQDfATJIL4NW+n2EMFEiI/DT72FtmkNo6HyM2h/go2AeehsgAirsluUYB8/EfvX64P8Pmp6zO+mmmjvrngJAtr6P/e9pAISOehCsdrT9fwnC7/tZwaf2mt/iZB22/vYGtFgVLZs/xkylUWQcr6+q59dQFfrrCMUPKjBqpCi36eqG27oFJ51l8zsttNWlOPKKfdmRFJaqKhhCAR+kLwGBorh5FfG+/Me7e8ihKzj3PLxEPd6GJb2rx9mteecU+DirFqKNvBTziAVIJFL64CeRrhXYodUtCPoq+1wDfMyBFyOlH/Rcdq7AHDQJ2ZVf7xb+NAaOQzEGYq9+FH34edgrbsqreLurlyKzCfRD5gWbTSuB37EeEa7sk6miRUvwpYrnxzAPvQ2ZaQp8gLt66YM3+l/rYBwg60o030MChqbhp1tBUZFamHisksjGDwBIWWHAI2wHSb7WUDnFJVn+/rHHDw4KWiVcqwDVsDBCBlubg3Gi+RYOPQF5ytWIyA7Uin1x3/8b/uZ3UHf59hd92/3oxydGhgjlY26hadlEcLN9678IHbNmPMigHcdJrcMoOxEpBXbjXMxdZ2HX/xu9ZGjfzDm9sCsxWodqmoEmUyQQTdzRuuB5Nmfe9gB1Ta3UlMe4d8o5ZN9Osf8pwzAKNOyUy/t/38h+J+5GqMhg35o9eeXK/8eG1npqYhVc/9RCpvzwfOY8s5SrHpvD/NOvpjnZyoRHb6YiWsp9Z96E6qs8e+Gv2Nhez9Q/3p6rbEsV8GHZBXOZ/9wDrNgQMFxr4pVoisa2i5SmaNTEK/OC8pp4JR8312FU6nnV9H58fbDTBeTDhg3jgw8+6PO1/fffnz/84Q9f8BV9cuhhg1VbLfauCGEoAtuXrG6x2KtYR3V9CnSFLZ0+JapO2Euhl34fRTXJrp0YTFhCDSi+RjlBX03vnkTfTWBUjkX6WfTqi7E33Yh0Ehi14xBq0P8hwhWI0AD0UQvxUu14FOI2f0DLH2/Lm6S0WBXtDTZqkYdaoPYH5d80KDBovwqevXEVqUSWH049kEjFeNAEZs14pA9SKAitJKC1C4nV/BR6xRkI38Kqvztf+K1iLIpRjUQghYox8HKkl8Jp+BX4Nva7S9H3HIu9Ip8Ki1EcCEoVVGOv+11+39j7D6IPPQWnc0Nu0x+IaAFmHKGkkNLs81lBqDjv34m+51mUnTwFO+vT2pHiiRvOZszMxVT1Qb39OqpCfx3RXYHx0slcG1A3tFgV6fZgd5NKZGEH05qqKii2JJXM8u+HVpNKZBm4TymjzxyYR8v2W1cH/Yuj55OyUhSZIexVd+UE3YyDptPyt4fw0u2U/2ga9uvbiRm6WZTY8Nz4lW4WzBhCTWE3v4gePxLcVqy6BYR2nUd23aQgAdbNVtm+L1064AbtIFrZqbiAtWkeod0WBMyRLnEkoZYgrTa0qtE5W7PunnBn7W/Qhp0e9Ia7KaxtkmDm6Fv6bnGSHqTqUENx7HYLuWr2N/L5yboeihPYKxm6jp9uQy+IoYYj6JpJCUFrXVs6DHQSsrsE3rQiikqL2LS2gwY3qHI7VgFmYQIjZLKxMej71bxUXkCetBTKCzpRBtSCquN8/Gp/QN6PnRqeJ8mEB1Jx4b2opoeb6GMeQyCz63O6L1rR/jjJFRhmTZCMdFvQag5DZpN9C7mF4ugHXo+7ahEym6DipzOwGoP2kR2tCx9sbqGuqRWAuqZWzvwlmLAAACAASURBVJtzP0/OHMdri4L5vyAe4rDzR6LqCoecNZw3nviQze+0UBAPMfzCCBOOGYtqqdx/+mw+aFzH4Hg1Jy++jIpoKYuOv57ld/Qc59tjh3PnSdO55Mnp2K7Dz+66MtcPvvScmQBs7WhjcGllQGvfZv8dVaPce+4NnHffdbm/ufW0Kcx5ZimLxkzlv+tg78fOjn7uw2cIRQiGxk1WNmb558Y0KxuzDI2beK5HtjmDEBDRBYr0cVylSyldIPQ45q63IdFBLUERClb9EszaGTmrm+5+XfRKkD6KXo4QGsKsDbKNoZIc5dc4eDqek6Hxt/PYfM9l4Nq0/eNByk6ZmvPX1WJVxE+dw5vPNPHSklVoUuTdi6oKHFwyno2Dm7OS6MfXB66QFJZHcjoHyUaLP928js6Ej3QSCBEom/sIzNrpSC8dCLt5SaxNc3JU9pxCe90CMh+Mxa5fgoKLUAtRzIHog2YhhY7xrQuQTgJCscBrfO9xoGgBVVcrQGZb0AaMxFo+Oc/n2X5zDtrugQVKdxBvr3kUJRQKKL1eBnPwNfnPyqAp+F4Gbfef47z/IKrmEhlQQkkszJiZi3nlT0/i7H55HvVWP2gWqS5v4H7s3OiuwHS88cde81rspNm8/VxQbSyIh3a4ymlSkNraE4yX1haxx3cH8a+HNyP3mpY3NlJDTub1LfWsbu3g/bYU/vALCX1vGcbBs2n52+NkPnqNkqMuYEOrjhh1K+b3HkLfZwLOOwtwP3ocY/+rMQ9fhDHqZogOwvjWJQGlvOx4rPXTgk2p04BEzVE4UQp6j+vaGUi3HSlCXW1OOnrFWIQeB+kjlWiXAvv5oA1AEoaSEeh7npWzNXNWLkYfehKEygJWVVcwDl309FV3YRw0rRct3XczCC2E/er1CM/GOHjmN/L5ybo+wu0KyFUVL92KFilBLw58k2NGK9KH9nRgXxa2M/hCIaOZGLHg81rZFvSWO+mg/9QIGVhZB1uJYroJhBBoikJYVWm1BEJ2IlQDpWxX3A1vfNG33I9+fGp4nqTDDZPxijBrZ203j81C+h7CqEJopQijCqRELzkMpNfT7ua3IYwijAOu6TUf2R8/g2JE0UeOC6woI0XIruey440/UnHm3Lx1oWzMLUx75MW8a6xrasWVPsNOGswBl+3JsJMGs/LPH5PY2MkLC99mj+8OorS2iFCRgZvx2SU0iJgVY8VDH9HyRJZwKkxFtJQp3zuftx78OE/R/d8PrUbpVJl67CWsa67L6wc///7rqW9t4prf3EbWsXt9do7tU14Y5+ZTruS34+9gxgnjmfPMUpo6EkE1nX6v9K8jvv7p7C8SiuDNujRpJ6DspR3Jm1syHFoTYc2Ldex+3C4cPDBMa9Jjc8Jj9+o4UhgYlecjpYW9eSF6xRmAB14C1BJCQ28FwJcKUmax107oEcAYNAWt7FSkm0SYpaCVYx56K/bKO9H3GMOAEyfT+NCUwMZm4yraX36c8nMXk+jM0JTMkpUlSFK9qkiqKqhva+cXs+/PUXt+dfU5VJcUf+3piN8keJ6Prmk58cH3/rKBUWfuydu/28ShYyNYm+9GLzsBMECNIZCglgBevvBblyK7dBpQIiOCqvnaq7rs0i5CKKF8sasDZyAdHeeVa/NFo5AIo6RvelrhIELHPARCIFNb0Gp/hLTbgqy7nwa9jNCucwAF32nGblgaMEfKJqANPRmhGdjLJ6JlGqkKV/DDcy7nmfuXMuq4Mxl48D6gmaS8Aiyp4CLRFIHh+/2skZ0U3RWYkuOnoChQdeFSpO8jpcqrv2mgZX1nUO24cCSu2MGc5YNm9rhhjDhmCK88/D6pRJZMspCDTplDYakGqgFZlapYGl/6XPbwjUz54fk88L+/5YrDT6T2O2dTfNRFtGNyxsyFLBx3HCMbA3q7EhuOPvSk7azIZuIpkSC5JQXGwHEILYY5dEHXOrB4G6X46zEGX42ilwEqvpcBLIQQWOum5K0FvptEUQvQK8ZgNy7DiP8M59WbMA+Zg9XdykSPSrx5yGykGe/1vPmNy5F7nr1dRf1JtNrjkTJQh9eKy+mUMcLbqNZ32OFvhCCi5fpgBxt/XdNwM22YBSVosRgZO01xKIt0FBw72GiH7QxuOEJYWFhaEUOKHFY0CvYJ29jZnoAcIE2UArue1tAIhJsmagRe5IIMQvFQy3fHeecP+J1bUaIDvpwPoB/9+ITQVEHI3YK0soR2mRcw2xQdnwiK7EA6SfxtbBylKEYgMXd/GOyPgxa6qovwfS+Yr+yOgDm36Xn0QUfnz6sHTSeyx/5UX7gEL52k4+2/UHXBPVieJCNV2tUQTW356vM15TGkkJx7dw+N/a7LxhAuNEglsrzy8PuMGrMnSHKJ24J4KNgn/X4dL9/zLlPPuIQiPcrGxMd5x04lsmimyrDiWsb8elLea3WJBkoiRdQlGjjvvut6iboBmIQpK4z1UlqPqlFQ+VJU2Pvx+aI/pfIZwvclaUcSC6mMqglz+OAIe1eE8LIOw/5nCC/XZViXsBlQXMCQUgPnoz8gUZDSQjFq8DtewqlfhC8VjMrzsdZeTub9nwfBjd+Os+XefGGMTXPAacQceDH26oexX54AUgZVxdemYxQVU3HuHUjdZODk39HwrbH87JanGHXlPRw//SHOmnsfg4+uyqsiqaog4zm5YByCLOIvZt9P1nO+rI+2H58THHwOu3AkBfEQLeuTfPDiJvY7eRiONxBz4MUIYYJTh/TSSBSEHu8lANcdmAPo5afnVc3x072FqdZPQzF1CAUVpW7RKCU8AGHGegloKZWjgSTZjyeS+eDnWI23IEIaUioohYchhIq1bhKZD84iu25SkDigx6dcCZflbFK6z6evuZ1Rx53EsuvH0dopyVBEfcbnnEfe4YdLXuecR96h2fbRDfUL+ib68WnheZJOL0zSCdPuFpD0C0krUfY9dRg/mvFtjrxiX8R/asVRwLW8YP4DjAJtG6tKgW1H+dO89Twx+R1eWrCawkyUrR1tNHUkmPPMUk45+Fg6VJ1Xttaz2bNY8s9HWDrlbKrjBbmxpg07Pdd7Dl1j/dXrsV0JWiX4ndibF5P54CzsjbMRXgfGoMk5kSOn8REULY7vNAfPn9fW9UxN670W+J1Itw281iDYV9TgvEJB33sc5qHzMQ6emRP/RGiIUGmfgnWKFt6uon4KGEUBxT1cAapOxE+CZeO6Kp3fkGAcggq57ArIu3vItUgxBaUVrG98n2jYw3MMPDcIyEN2FjdcQFhkyUiTvcsdVm9V8Y12nExBcJxwQEJNOzoF9kZ8PQ5Aga7TnAk+VyHSqBV7AOBtXPGF3nM/+vHfoMDI4Lw2FYGP9drNIBR8aSDcRrJrr8oXoWxYhoKNlAJwcV036CkXKkLRkVYbzrt3AWDs3se8+tp0hO/S8ueFNC6bRPJ/l+FaGdIdScyCGEi4ZfzPqCkP9h015TGWTjmbzrTF9HN/wv67D6auqZWL71hGVu1peYqUmLlEbffvXnn4fUYcM4RUIsuwAbUknc7cOpK793gI1/JQNUFTRyLvtZp4JW3pJNC3qBsEVfIRVUN5cvxCll/3KE+OX5gLuHekwt7pdX5G31w/vgz0B+SfJaSkMqoyotxkQ5tNIu2QtRwyikBKn/2K0uwVakO1OtBxkS3vINwEbvKtwHdcrwyszJzNvdUnGx4ONlnbnq7bd9lqx9/4bNcmS6DscU6Xmm8rejiE73kkbcm0X7/IijUbc39f19SKFlVzVaTuynj91rZcML7te93+auHXDp7nIwpUjpl8AD+a8W32O3UYjirpyHp0ZktAL0cxKlC0SKAKjAldFTmn5blATKorQAdywXkuMO+i4m4L6TQg3Tb0PU7v+WUohtR00MA84hbM79yGiO0ZZL73vgBrw/ReKuoiPCBIRjU9kf+sND+FXn56kCzQCpFC77PqXlJewdjbnuTe59+kubUN02tn6YmVPPjTwQwo0Jnw5HtkPfGpVLr78eXC83ws38PCw/K9/8hwcIWkYEDQ61cQD2Gn3NymattqOYBZZJDodCkvquXxS+6mrKiU8+67jssfuYkBhSW0ppIcWTuKlr+2UBIp7qFX7sDKL5P1EFphHyrqN4G0ya6dEDxf1ReAUEErQwg/CLzVktx4N2tnoURGdIkkmUivDaGEEGoJwoiiVI5GCh00LeDDaRrayPEoFaMDzRKh5NTegVylyZcS87D5hI66H/PQ+RCpyPVqGgdNx0k0U7/kAjbN/QmNS84jkqn/xrQ1ZR0fumimilDw062okWKiJWVsbl6Jrktcx8DzfVTPxfAcHNMkIoKxtFupjycFG71G7Eyg+2KYXRXyjIMmLRQlSARGNZ2GlN91rhSipArMKN7Hr37Rt92PfnxqKLjBXtTpgGwr9vq/owi7l+q63fwURvUvQHoI4YNvB24SzU8hhIBQORIFffj5OCsXI7OJPudVfIcBP7wECGjqnZ1JCiImoUwTJWqWf6xYzbxLfso/Fk/hiVmXkLEdrrn7N0y/73dMPvO4XFCeyQbPd0E8hPTZJlEbIJXIYhQE7EJFE9RUVuQKG91/9+2xwykYEMLRHBacfg018WCP1N0Pvvhvj+T+3U1D3x6apmF6EQpkEaYXyVW/FQS3/eRq/j7uQf7fOQs4YPBeOwzs+/HVQT9l/TOEk7HZuyLEysYsUdXn8idWUd9ucdSwGDceYpJZdhVy6EHEvnsWwlAw9r4Au+FBzOoLsDbfhTloEtameaBEMcpOzNGAA0riJKRSlHc+oVcGInCZgIYTCO9IjJqjsLUCpN2GYsS467KT6djawM03Pco1j8GKD4OgvKY8RmEsjFCDKpKDyy9m38+8S35KTXksLyivKY+hq/9dtVBVBVjteJ6Nqhpg9lPfdyZ4nk84btLZ3JHXumDZPpYd0Kg0VVAY2oLbsTLo8zKjmAPHIbu+RrN2RlC1265qvkNhKq8NpWRXjCPvAt9FhGMIaePb9TiNDyGdBOaBs5DpLL7vBLRetTAnWOWn30NIB4nW57OCXk1o17nYa5/B2OUEjENvx33v7jxhOCVazp1/eJnTvrMXQyLteG8EVfTScAULjpnGhL+kSSVtIoX6F/Zd9OOLg+f5YChEy8Iceem3EIrgsPNH8tLSVXnV8nhtEdWnDOWyZ9dQ325RXWxyywmTmHDMWLZ2tNKZTZPMdLJv+Z6IQzrx0y6hg2Ziv3Z9Ttdje0GiIlPD97y+1YeVUE/rxzYe5KGhtyL0OEKAtQ2tvdsVQehx7KbH0Qf8CMUYGOiQ7DcekMhwBHvL3cFzNWQG+j6XY79/P/qQ43BWL82np6+8A2O/yeBJrH9dHQTkgL73pQi9ELszQ+OvJ+ZZCjUuu4ryC+6lk29GD7nuBhtfTbpI10IvGoCiKDidKwFw3DAgcwrrjhkibAd941UlOqqQfJRNMFjshXQ1zK4KeWfKgijo0sIl8CLf3BkEB4JOhKhALR+Gs2EFpuxPkPdj54aHighXBE4VXW4q1B6VN+91z3XWuqu38SmfiZQiaJmTLqAjwgOwX74SEYojzJK+hSeFgl6xC4MmPoIaiuJkLBrvuwy3y4v8yjHzyOiFrF6/kTlPvExTewe3jP8Zcx/+ExMXPc70c3/C9Pt+h5MMkrOHXzgSzVRybX3d6K5+H37hSJAC0zFple0cccU+qL6CUAQoYEsfVRqUFcW4+ZQrKYkUUhIp5Ibf382KDe/l0dCdT1jwUlUFJRWi5YksGxMfUxAPsWDMtdz4z7t6qbX346uF/tLPZwjP88GH0pDC5Kffo749WIDPHBmledlVqIWlFH/7ZLbcO56Gh6ciwoHHs/TS+B0vYTfcG1jVaMW5AAN6bGwUrbCXcBVaBc4Hj/aIXa24CWv5RPQBe+NuXUV7ooUN77xC4YBKCtwOHrnqBJ694RyeufFcfnfdGZheGroovq7nU9fUiqoq3HbpaXnUntsuPe2/qoCoqiDd9BELzz6UG36wKwvPPpR000c7ZTVFVQWqmwRrK6qb3Cmv8cuC60kyXjV6yWikl0JIG2vjLKT1EcJtRHopjEETEaHd83zNnaZHMQdN2m7cTsJpeQ4pXYQmEOEypJvCtzbgbP0jRuV5CD2OtX4qSmEZQvGxNy8OhF42L8aoPA+l8DB8uy4ITvp4VsAiu24yes1B+HYnQnpoIy9FiQ3P9fFGIyY3n/tdaouVXDAOQaY9snIG1xwxAKfD3qFKdz++OlBVBVNRMVExFXUb1oPElS4vLHybP0z7N68/vob9TxlGuNjMVTtqfzCEKV3BOEB9u8XEpz8gFq5i39LhRLwwyVQnMb8N68+X07ToBOy2TsTQS5F6Jfq3b8YYfRPmEfMxRt+EPuom/GQbbtOG3HPRje5WEL389KBqtA3zw/ey6BVjsdZP7zXezYEX43suRtnJoFdit76I3/ES0m4g+9EEhBLCGDwdJfZDrA3TkKm16IOORvoOMpvAfvV6rJevCGwzs4mgKo/EOHgGPgJn5WKQYKdtFFXv01Lom2AZKKUMKOtd7Vtqth2AUMUQbCdLgbMJAKvLX7w7ILc1jUIl+P+siDAs7rAmlQIE2VQs10Pe1h58hoYbHDeq69R1BL9TRGCVplYNR6ZbkU0fft63249+/NfQuvZP5iHz0EecjwwNwBw9N2iV0StRIiMwa2dhDrqm9xq+/nqEkCh6Bb6bANUHpzOYSw+Yii9Fn8KT9qq7kO3rUOnEefdOVK8NtbAUCOaopmWTMKVNlZbl7gv/h8NHDmXiose55KSjqGtqpbQ4yr1Xn0N1VQlHXvotzAIdRJCo3bb6ffiFe1NSHUX6khfnv81zM9/g3Xs20dGaIq1nyPguGdcNCl22T1mojD0qd6E0WkLUjHDDyZf3oqF/4s9VCl5asiqPQr9q2Ubm/WRy0F/ej68s+gPyzxCGriKQaELmNm8AA0LBZFByxFk0/vpq3NYtWBtXISWB0roaytHVrfVTkX56B/6zDsbgXxLe82HMobeBUQ2Wi3nAdYGi7+ql+K2rc32Kxi7H88C1FzBkn1Gcde1tiBcW0jj/ZAr/eA2D1Q5+fc0ZLDxrNOnGDzF0BU1VqCmPoQjB7GXPMP3cn/CbGy5h+rk/YfayZ8jaPRuu7YNX39/BhGK1c+/lJ5Co3wBAon4D915+Aljtn/0X8H/AVylx8GUhm/XxpYq9/hoQCtJJYK2fSnbtBKx1V2JvugUhJGgxhDk0CMydRJBoqpkQjNvB1waBRuUYpJcJKLvWOoSfDIRdun3Oy0/vegYCaq+x6x2Yu94WBOqb5mFWX4Cb+hBw+35WfBtj4DhQDZRwFKfx32C3Y+x/LeboW3DefxD7+dNxll9JScxADNgv/xiZRnaL64SjOkkNtrSncLQsjprtHxNfMaiqgkx5vDD/Lf447d+8MP8tZMrDVbN0+O3c8c9ljBwzOKejsOI3H+L7Pod3URDVAj1vPt+nKsoNR5VTJjtwGlroeNHmx8MOoHHZVblAteVPtyMx2Pqnu5BuG3bzArIbLsduXoDQbFpfuI/MuvcwdpmDucvsgH6+y2yMIdNxmh4FvQaj8sy8RJTARTFqdtAC0oGimkgkzpZfoRfug1J4GNLryG1wpbUOvXgUSuyHCD2M/eY8hPQx9puUv7E9eCZS6PhqBGfd0zgvT0Df9UTs12egaYD0c+rF3dBiVd8IyzPHl/gS/G49lWzAIosM2p0PNr7JnsXBr9NWsHkPWxk8VcMRCiVKwGRr9aKMKLVZn/RI+w5WZwwjFATwmbRFVo0RsYPvOKrrbEh2V8iDnlO1ajggcNf+64u45X7041NDUwWFogF/+eVk/x5YnWInA40L38UYegfGwMsDAUu3pe85zUvj25tB2gghA3eHbAvWy1cgrK04dS9ijp6L+Z07MQ+ZjbP6XvzG5YETxJvz0IYci7tyLqVHn507rtu6BbuzjWwqSaq1kSknj+bwvXdj9O6lvLLwAvYaGKEoEkIPqXQ0Z3jpV+/y/K0rAMlRl+3LMZMO4MDTdscs1Gmr7+Slpe/mBcYrHlhLyDd7KZ47tp+jniu2ieH2pqF/YuyIQi/0fkG3rzj6A/LPEKovIOsQMVSqi4MFdu/qQqKRMFqsCjVSlNuwmYNHIoWG0EqQnpOrIqqx44IewD4qJ769GcUoR8qAOi4tC+GmkIqO/co1OTouBAFFuiPJ+rdf4ccXX0vn72/Ooxgmn76RH198bRAgTzgRt7MJXVV4eOoFpC2bprYOfjH7AU657k5+MfsBmto60LqqSoau4KcaaWtYz5YP3uLJm8ex5cOVfQYqnmfngvFuJOo3ID0HQ9+JBLO+IomDLxud6TBG7Y1IX/ZZ+Za+xK67HYGLlAJz19kY1eNBOlib70YYZRgVZyB9C9zmHEXX2jQvZ6Oml34foRbmPJetukUIvxXUGHr1eIQeD2js8SPxrbo+nxWhhLqCmUvJrp2AvsuREC4HAdLpwNjrfIzv3IW+9zic9x/CGHZa/jHCFdi+4OXmDtY0pWnocLH8MPf+87fUp7f0B+VfIfRVUXhpySrWbtrEqXdewXeHH8ydK35N6akhDpq4G0dcsTdX/vFmMlqW747bh6K4mZvP96mKMu9wkwFPX0b9LT/B+vPlHPijEEK6eVVja+MqWp5dRPmpk3AaZudXfzZMo/SYsRSNPgUhLey6BT02P0Igio5GUY3elfD105D4fY93tQBr40ysdVfid7yEtfFmzJrxCK20y3IonnM7MOJHITHQv3UJorAcVAXj0FsxD7sDc/QtSCOORGL/9af4m57tUjEuD3pB3SxeR6KX1VzFmHlktlMJ/jrCcoMNr++5hHQdtyOw1wuXDeLVj15kaDHgQtYLkhOBwnoBnu9RogQV7lavgBGlNj6Cjf4W0q3luQq5lc6Q0iuJZd5FahGiuk7S8ZHSQNDVmmZGUUqH4Kxd/gXffT/68cnQLeaWo5SHYgjasepvJPPBz8FalxOm7G5r2xbda7jreoGwrFADqnpBNcb+VyM9B61yFNbyyeCkkFYbfuvqgMbudARzVpd+hxotzh1Xi1URKSyhsLSCR647B0WBG87YD/31q4ituBjx7wlEvHoy0qYgHmL02SM48vJvoZkaUkrMAp2iijC+41NcVUD5HiV5151KZGlqS7AxWff52ZAp9CkgJ/u3JF959AfknyEEkE06ZNYkmH/yCI4cFmfK/+zKJsukbMwt+HYmt4kpPfpsSDfi+y6KFsnRE42KsUE/+fYe5IMm4TQ+BNIn+8HpWGuvRDF0pFkcVNr7UsrVQ8Srh1BYMqBPimFhSWCbkqjfgOfa1NfXYTkO3xpaw6+uPpvvjxrBsomn8tyssTx9/VgKdANVFXRsWcPiX3yP28cexlNzr+A7Z1zKnxdP6zN4VVWDePWQvN/Fq4fQ0ezgJt2dJijfUeLA71eWz4PrSRxtCL5Qe1Fq7eankG4jRtnJSLcV66PzyX5wNtmPLsFaPzWg0Dpt4FsgNJymR3MU3W1t1IRagvSzwZjf+mwgbCUlihoC6Qa+y2oI6bTiND7UOzFQOxOr/p5eQZASiuDbSaz/vQTrX1cjvCzoxRh7XQiqGQhdETw72kEzuON3r3JobZTfr2zg7GVvc8nj73LiQadx63MPkCXzpX0H/fiU2EFFYUAkRl2igasem8PRex3CT++fwBF3nMmH7etpTibQLZ0XF7/Dm/e8y9wf7EF1scmEb8ewnrg6L7nZ/PBEbESvqrHX0UK3ReC2kE4DWskAFCWbJ+oWOBhYGCWjkE6ib+YHeh/e5LOwu3QVtn2vtJvJfnRJV5vH+UhhYgwch5QKwlCxWxaTWTMGq2E2iBSEK/ARgIf7xqzcsUS4AhQtWFNCRagDBqGVDqLqgiUMvPI36MddjWXEvhG6INmugNzzg4Dc62wBQC0s5Z1Nr1JT5IEDGQ+QQQ+5GynA93104VEgMrT6UXYrcTBUyQZZR2eiCiF0VE3FymRJqyXofoqQKojqQWDvylAuIAdQq0bgNX6YSwj0ox87ExTp5vV363udi1V3U89ct43Ya59tbbWz8ImgRYcj9AEgPfSDf4m9KnB+cNf/ARGpxhx9C6KgKmD2HHEP5ui5uFuW5wJzEa5A+sG8pMWqKDtlKok/30EsFiNRv4GCsIa/fbvauzdRoGXJJG3+fsdbuFmPt57+iD/OeIW/3/EW6Vab5fe/x9/mv8nu36lhl0N6kgkF8RCNHVs/V8VzV8heAnL/0d6zH18Z9AfknyEk8NI9qyitKSIW1pl49FAUIfCloN4vwSsfRtmZc4NqebQYoWk4m+fhSw2j8izszYsBLwhcvBRGzYRtVNbvDTZpXV+ZdBpASPDcgHZ48MxetMPOtlamPPoCpUNqiex1RN61arEqOtqCxTxePYRkcwM1sQJIbUVm2hlcGmP6iQfwv7NO454z9mXJed+ho+FDVDfFvRNOzKskPzrtPA7+yVn4ntO7R9Ms5rzbn84F5fHqIYy56Te8/9dWXrpnVcAq2AF23Pf52UPV+k4cKGq/oNf2sDM2GRlHrTh7u97usQi9OvAAd1v6znrrJUih4tQvCgSmBk3KC8yD98RBGDitf0MvPojsuslkP7oIa/NiFL0UYe6CAKTX1kOJ704M1EwAtQS/46W8c3fT2FHDORcCe8VscNNk/34W1ksT0Pc8C/N/HkeMuo32tMFZR+6D50l+dsBAIOgdnvT0B5z3nTH9aqZfJeygolAcjfLb8Xcw44TxVMfKgUDxdmtnK1O+dz7/+lXgO5tYn2TzEx9xx5G7sdcAs8/kZsrxiZ42N69qHP7pHGxp9PkcgAqyJ1hXIiNyNoHSSQRq6X31l7uNQWA9aCKhoQsJ7ToPqcbwO1f0fq/XBnSrGP8WIZSAImp91IcV4VRQuzZ0nh30kUNOm0QiMA6eQTrZyaJLfkzd+nVMOmIgc8Z+F98owFML/ptv5iuH98J9AQAAIABJREFUjNMVkHsOYdPA7dyK0E3aky24nsOAAhdcsD0wXBtV+thmz9grUTpJeFF0FfaIO6zJtOF7OsnGoRghEyudxUUgEYTdNoqMrsq5Z6DQs8FXq0cAkFr98hd49/3oxyeDL7S8IpESKc9LMG5bFffT7/W0te2xjNDQ+aAWI9x68DrIrr2CzPtnYK27Cn3PE1EGH4s2/Fyw27CWTyT7/JkBjd1JYq9agj7oGPQDr8fd8CzGwdPR4gMZeOXjVJ23iMRzd5J+7x8oQhCvHoLYLnEAQVAufQfiglQiyz+XrGLfnwyltLaIVCLLvx9anbM8e2npKkb8T7BvLIiH2PesXZjzt6Wfq+J5tzPOkVfs+8nsPfvxlUF/QP4ZQnqSUJGBqisIQFcEadunI5XmzFlLWb6mifnPf0jVBfegRAeAVoB0EjibZiC1UkJDbw2oOXolTsOvEELH2ji7yyIigVk7EycZbLyEXgm+xHnteqznT8d5/0HM0bcQ+t4ytP1mIIVB4bp5+P88D+dfV1J+/IW5oFyLVVF0wrX8/q4biVcP4ec3Pohuhll07ne554x9WXz2YSTr1/DcndN7Ubg9z+qzkhyNl9PZ7OT1aKqqgudJIuW7cdkDy5n4yPuMmfUca/7mk1jfSSqRRe6gqrKjvs/PIyhXVYGT6eCMG+7PSxycd/vTYBb///z1Nw+27SEtG0+vxdhtMeE9H8EYugCplCCd+mBM70DMzW56GqGVYA66BqNmQi7R1C30ZtbOwm78Nda6K9GKD8mJvaix4zAqzyS79ipkdg1I2WO71tXLbm2cjdDiufNtC6FX4nduhnQDRs1ROS9moQWb5W7dBdKb0fwOUi2N/PmWCWhtGxlSYrJ3dWBPVN9uMSg+kJBufoGfeD/+L+ironDI+cO59pkFnLzoMqY9vQgpJd8feRj3nXsj/+/VZxkQieVV1RPrk7y28G2EovXZP12fdPiYCpInL0S7+Am2nnAHV/1vll/+uQF9yA15z0GmYjq2q+bmeiDfJtBry43t/KrR9CBR1fgwilkDejk+Cm7iz5iDpvRmVDU9mrtGvfT7OYpoNxNlW0inASFthFBxP3wEfZ8JhI56EGP/KUg1BEKlrdVl6ZSz6NjaQEnlIKb++WMufeBlIuW7fSOq4wBpO5AwtjybwkgYrHbMWCXrN66kyICw4YMjsDy1R2Hd6EnqDlDbaXQDmuvwUotNnRI3UkfT2m9hmCGsdBaBT9IcSjy9kpgZ/G3a0WCbgFwUVyMiJaTfe/GLufF+9ONTIGWH0Q+a1ROUK3remhzsD3rmLOkkEFocX7pk115B9v2fYm2YiXATKNH9A/G3wVcjhYO25xgUVcd+Lb+y3d03br85ByUUxxh5EVKC7FiPFlLws52UHn02gybchxENc+n9L4Bq9MkuXd/YTtbzqT2kErPIwA6r7H3BXhw289vsNXZPwvEgUZZKZFFUwQ+mHcTgM2OM/8NM3tj47n+0Mvss8GnsPfvx1cHXX4XlC4RQBfudNBQr5aCaKpapcv0zH3DTsUOoa2rl+dfeZcrPjyMls4Q9D90MBRun9tfAzwRizkLDHDQFa9OcXNZQMQYikUjPwijeHwofRAod+6Pf5yYkv3E5VnItxrfngh7GeWVy/mT12jQKjptDZtS5xEuK2Vq/nh9fMYd0ewLp+zww6af5wfeEEzlx8nxWvvC73P0l6jfg2RZ7H/mTvN/Hq4dQGK/gjceDqopZpNGxtZGo0FFVHdRidDPGisfX97KOEKro06ZBk4IX+uj7PPKKfT97VwernbsvOpbCAZWcOHk+keI4diZFYbwK+xuy0fy0sG0P284ABqoawoiYKICqaehDZuJsuH6b8VuN7zRhN9yLn34Po+x4pFRQzFqM6okILYyUYA4chy+1XLWvO3BQIiMwyk/rsYBSC/GdJoyyk7Gbf9tliVaC0ErwRQT3oycwd5mZqwIKvRJz4CTst+/FGHEh0mrF2G8y9rtLA3/ULshMI8IsxXl3CWbxcXznjEv5y13TOXbyIsZ9p5aLHltJdbGJJkW3MUE/vgLwPB+1q6KAD77iceVTs3l21T8BqEs0cMED03hy/EKiapSrjj2HZLKzT6ub1maNAWfeDp31KEYY386QKajkur+1cuXRcc58dGP+ybdAy9FDie+6iJbONI2dPrf+McERQw3OPrA0aK9Yf32eTeC24oa5sa3HsRt/jd+5IqCde2mya87NuW1IvQxj8NUIAC2GXb8UIOgfVwsRWqynIr4jK0KpgFAwdj8DpI/vpsG1IBLj5d89xm9vumybRGUc6clgLv4GzZHprgp51rOojpQgG9owSipYufEtartdSR2wPEGsKyC3VB3coPWpWm3hbXs3shjsVRqItW0tXkF18/dBVpNKBiJxDQUHskfiMfYKBwrRSVuhLNKWuw4hBGrVCDIfvUbUySL0fAZIP/rxZcL1JB1qJQWj7wi8yOnZ10qnIWABKWFCQxcgnWaEXoovQtgfXbKdg8QcQrvOzbN/NGtnIrUi+P/YO88Aqcrz7f9OnbazuzPbCx3BAihqolESuxjFiCgW1CA2UCSCfxWxAXZABUVUREBRMVZQscaa2NDYQFBAAuyyfXe2TD/1/XB2ZxlYEkxQ8XWuLzBnZ845c+Zp93Nf93W5A7BNdnvbunE73gCWgfHtAuxECOXgqSh+Fe3b+RjxOgRPEf4DbyIeNvF1WFS2H9cH3cg9iz/hsjOH0e+4XGzDixELMeujEO9saKY0x8Xs4fsS7JlNsk3DMm0syWLsM1PZGqr9wVZmkiQ6awoLEJ0N5EyA/etEJiDfjbAl8AbcvD37S343fn9M2ybPq5Dtlhl6yH6cO/R36M1VqKIObjf4ctAaluEqG0+yah5K6TiHiivnopY7VjW2lcAyw+h1Sx2LNDtBYuOE9oXYFLSmVdjN3znXj9chiCLR5nrULmg4HlVEUV0899n3HNMnhyXt1PMrlnyw06z3tgiW9qChYgPDr76bqnVfEare4izQ5izj+78nCG2OEOyZRb9jRB679rjOv9+7HLmkH0MuGcAHDztBti/o5ojxA9G1ZjCSO/qT76Tu88ewoOqoH3c2Ikakjt/42iZw/f8vVLQ7kGyI8cH8b3BlK/Q9o5yodB17lwbArCJZeWeqvlVQirGSlSQ3XdtZ7101Dyv8AYJSjNrrDlzdryNZcXsqcFAKz8Y2WtMCCr3pDZSiUSj5w9r7SRTbzgZBQSk8FGw/asFEBMWDnWxD+3ohJJqxjQTYzuJY2Xs0eu0nqe8geIqwotUovU8lVw4yf+JRnDVtAS50euWILDlnIC5ZpnVVI0X7qlhGG6KsIrtz0PTMBLonwzSt1EZe3I6kgvEObA3VYlkWumlR6i3ByNLoM1big/lrUuPV78cOxJ2tIEYaqVs+I+Vt6ztzJvlZCvleldIcF9WtSQaW+rng0G4EfApx3aZFyOKEBU4fGFSSxRl9ALMJW1Bx9Z6BIKgpm0C1+ELHaSBvaPtGUw5a3ZOYza8CjjOHIMiI3n0dZ47KGbj63IttGyC4ABWlaDSCFUktgF297kwF4R3slQ72ScpCEwFba8WWvSAqtCYNqkIeCpI+Bh51Gvv+/mSnhGfbcfpXhpjutKKEqeFzuzAjjbiK+9KQDHFwoQvQ0XUVy7LxJOPoqjttA7lEcmrOq4w8euXU4JEt1jSbHNzvQ9Z/U0Q45JRDJAU3Le7+9EtuJqhAKAF9SYBgge2wxKTy/TE2foS5eSXyXuklaRlk8HPDMG1a485GUY6kI4qetHUtgC1I2HYS2zIRxa7dhWyjFUEJtm9O+rGNEIgJ1P0vRvt6QWr9m1Y3rrWgr56H63d3on05E7RWtFVz0pJUxhc3o/QYS/3bL1NwyiwiLQ20hZowmnWuOXcYVnMl8y49LbWOvWL2ci4+fH9aExYx02S/i/ZDFWDzxzX0/m0xL1w+F8MykEXZCcZ3QfE8xQSd37kuHjJ2AFKGgv6rRIayvhuhmxa2ZRMNJdDCGggCU47phtHWwLyLh9IvKEO8BTnZhp1sBATHN9Y2UPKGov3rKmyjFWwL0dUNp85QR69bilpwquPdvE1dYHLrHSj9z05dX/AUkUwkCNVWd0nDqfz2axaNO5Zje/vJK+vPpY98wA2vbiK3uLzL+umcwlLGPvAKPQYdQrC0B2dPX8jrD92MIEr85fGPufF1h7KYU9yf+nWOJUu/I3N4/LrTd6C6G/Fm5GyZY64czMnTD+XYqwYTaf0X940+rGubMRHKBuVx6Pll/P7SMg49v4yyQXk/SovdmfBcpn5817CtirWAQI7txWcXEor6sKU8bL29HrV94a/XLQE6/UaVvKGp19qmKdi24ViWyQW4ek5zRN7sZIreptc/5fSHuqXtwbWEqJaBlUQU4ujfLsBOOpO49umdaJ9MhUQz6uDJoGZjbHkdW2txrAHLj+z0Jh98Ncb6JQieAkRJ5ZzbHiO/e19cXj91TW14BY1uOQpZJS3cP/owbv1jL+4ffRitNY5tYAa/DMiiTHkwvaRhW4qhadrIlorpNzli0iCGTT+EY64cjKgIRLbWptmbGc01RJ++hpt/X0C2ZXHPCEfMc8IRPZn19kZHDPCZb4gkDZZfMJBHz+zHfSeW4JbiYNajbb6WxLrRWFqNk+lu10RQ8ochunthY5KsvLMzGFeKEeQcktUPoxQ6Y79DN0+CFQdMbG0LguRNBeNAu/jh5JS9ptawDHfvmbj7PtCuUbIAtEZINLG+qpme58xk/3HzOPG6x9BVAVvNwXblYcrZv9pgHJyA3LYsEpaO15eF1lKP5PbTYrbRP0fBtkHTXViWmRJ0M43OWtIy2dFtqdTzkUQYVKDxSZWMO6cGr99NPNqGZYFH28o3ReMRbYMb9pKojzvPXGxXagcQC/ogevzoa9/+aR9CBhn8QETjKmyvM2GBbYFWeRfJ9ecBcte6GXbS2aTs0KvZOgdBdKE1PY+yz5+d93XM31teRz3wWowNT7W7QsRR9rkQQfV3WSsu5xQQW/M+VQ9eSmPlVu7+83E8PvEU/MT44JFbOPWa2UxY/B6nXjObtx6aRlN9HTe/th7Tgikvf8f5T61C2i+IoIgpW7MfYmW2MwcQOSOZ/qtEZhW5GyHbAqIkUjooD10R8MnQWwzhXn4NVbNOoeGRcQRycpC8OUhuDwK6sxASZCfo0GsRRAlt8/VodU8iyDkIUq4TrDcsc4KQbeoCbb0Wwe3UowmeItTfTGP53Nt5Z+kjyAdNTRN5kw+ayspXXyBUvYVFk04jEW3kjpXN1DU28sIdV3D29IVp9dOjbl3MY9eczbO3XsbIGx5g1C2LWTH3esKNtYQthU/bshACpchZAXTb5g/jBuILulE8dJltN5MxTMsibhokMEnGQyy84pSd2ozZkk2fI2wev3Eod43am8dvHEqfI2xs6UdYDHYhPJepH/8BaGcz5PXM5tDz9uajhWv49L61vDvtK96Z10LcNQNXv6dw9ZmDVrtgBzVoQfKnvxZEpyb8+3FoVfc5KquCukNA4Sodi+jqgW22kKy8A8GOOZtbB18NsgCijfqH+3APfRzXH2ZjqzkIJFH3vwRBdYM7gJ1sRT3wWsf+7NuFjpiVqLLkpsuZO+ZI7r/gKNrqKglmuQnIOvFQNZHGavz5zsIhVL2FxROHo8ebiRpJNAyUTHC+R8ONh0UX3JYKysuDxSy64DbceABHU6I6VsOw+8ax7y0ncuyDY0iaGu/fvwrVZXcp6mZFEyQkkbhucsWRvZj26vqUd3meV6GusYmzb5zHyVfOItzWipydm6Y0LAgiWu0Cxyaw5FKwdbS6pQi2mb6h1XMaWv1fHUZJe78RlGIQpPQFq2CnZZsc4aQFuPvMxrP3kyj5w0hW3plyQLD1EHaiBUF243N1tt/ywgCKnMnWdCCmWdiGQzXP8qqYsVZs2UWrGaZXjohliGi4sA0Dt57AcHuwt6lv8YtxAmKYTYbDPjusLEFrUmRVg0JuoYFt6TRv9aBYEUxBZmvOMZxUKBCLO6weUegMyAVRwt3rQPR/rcRO/DiKzhlksDtgmDaSqwxb7QNyIYLaAzNqgwmunjc7gTdCF7oZ00FQU2we6LSAVPKGImaVOD7kh88GXzlK71PR1z6cskATZA/al7MQlKwuk1SC24+r+4AdXIdcssgfRk1g2cxJzB1zZMpNqMSvUN2a5KZX1nHBod0coddX1hG17f9OePgnZIJmsOcjQ1nfjZAkEQQoPqU3ly1by9Nn9KD+8avSsin1T06m5IL7MGM1yIhORqTucdSic9qF2jRsvRaz+VWSyc0ohWcjSP4UrX3bYEZQihFcBbiOXgKiTF1lLb85+c9o8ShNLSZmyThcbjdtoUbeuXkKw/5yGzXfr2HLqpUYusaIvVx8svBGfnvKaLLzS5iw+H0s06B+8zpenjOFLatWArBo4gguX/guZ9z4IN7sAIZg05821jfmsF+hgpnQyC/Ncuq7tRaCpT3SgvIOqntJPx+IziLy39qMyWDGW1g4cTtf8InDmfDohyBnszvRITw34dEPsUz9V0/L/MFoZzP0P7IbWsxIm2CaNod54+4ww6YfitsXTQUXHehQV9/Za1sPYWk1iEo+yco7O2lrZphk5R2oJZeQ3Hyj8zkrgSBIJDf/nyMg17AMteA0krULOkURw59gVv7Vmez3n4yt6ZBsRfv0ppSidHN9HavfWQ506imMf+Rt5l10TIq+dsE9z5OItvHyvU4/0bUk39U08+y7nzHxjOPpXRAkaey8FkySRDRRxLBsZFFAtaxM0PMTwTRtSr0lLL98Xopi6MaT6u8J4lyw6Hq2hpwF4NZQLaFwK9FQgkTURg6UpAXlcqAEV7aHsGVzzzubuHVY/1QwDnDZYaVMuusRttY79cE1zVHKyx1rvw4auWP9FwKjhcTGianP2snNqGXjEd29sbQatKr7sGJr01wJXD2nkax6MG3BaiW37lAnbushrJbv0dc9hXrwxPRAv+xq9O+WIfc4mdaYk50pLwzwyLVjcElyZixsR0w3sQznt81SDCJARBKxsCn3m6ALaLaCOxlDgDSF9Q50l+vYrDlBx/4FSXyKxfsVCqcUOEF3/UaZvO6QHVvDxryzKGn5G70kJ+AWiKady9v/MGJr30f/9m+og0/98b54Bhn8jxAEkYawTF1VNcHXb8BorqHs0vnIBd3aBY1tkg3LcPW+E4ywI27Z+CJq4Vldi1BKuYCEnWjEFmRE1U/yq7tSteDqb6ZiGQknUy4qyPtPxvh6Rurv8oCraHjpXnKPGE3TinvSXIfA5qmpF+7gJnThgvcAR9w12yOn/p80bdrq4+QHXEhei7AWQ9luXukS7Q4g22uVIJIJyn+FyKRydicsiNrw0IdbmP7Hfrgwusym2LKKoASxbAlXz5uxIl9gCR5cPW8G0ZVmB9GhHm1johactoNX42dvvMqtpw/h+1WraazaRE5ROfnd+rL0pguZOepIbhlxKPdeNIzV7ywn1tbMsAm3MfCoU9CRKfEr/GHUBD598THaGmuYO+YIWuq2Mv+yk1LBODiDUWtDNbNGHsjcMUfSsmUdb80aT3HkeyTAneUmbtoIXgXRk8OFc5alZZs7qO6moeNyO77jO6WJy4565U/tC26aNqacnaFl/hcwBJsDT9uLlU98RyKsd2kxhQjRhAd1O8VpV8+b0Zve2Ob19O1e34ze8AKWXp9SU09snNiZ1esISrpNBqUIy1baxWBmOcrSlTOcGvR2erxacDKu3vcgKEGSVTMQJAFBzcZ1+GyUQROxXQGW3Dg27f5D1Vtoa6xNm5wXXXkagZLunHPbYxx9/lVUNrYxbdGLjDnp98x55k1CiQSSCaoi7WDdJ0ki9ZrFmKWrOGn+PxmzdBX1mvWj2vplkA7TtFFMNx47C8V0p/V33TJSwXgH6sKN+IJuvn6jkcCIO9PszQrPuYuvXmskUh2lKapR05akNMdR4R8+qJCybCUVjAM898kGbFsCuQhX9+tSteOubpNTQXoHrNhax6oMG63yrlQw7up5M8jF7b7i7GDzp9ctwdVzenpf6zEdfd1TjpaCEMTd+x7cPeei5o1H/24ZSu/TwVtCSXEPPnroOl64bTyluZmNyW0R00zQnIDcbcYAaMKZk0r8GoJho1kyvqQTOCdd6g7n6CHX0Wxl0Wr7UCT4bUmST7YqyD5n3AxVOxR3f/xbNCmXDUJf9nM71xSFcNq5lPzuiLllaF+/jG1nfqcM9mwYpkWOR06ti0VFBL0NK6FhoaIWn4dW/QjYGlrVPMzmV7GM0E4sVIMkP7sdffU8iNc6XuUDx+Mach+uw2aib30PEg2OUFtDJY1vPI7y2xkoh8yFHmOpWzaX2Jr3kbICaa5Dp9/+FM2t0S7Xn9FEuzhjjou2uJH6f1VbkloF2hQRzVK4929LGH7/eKpjNZ1lmF09j4yneAbbIJMh340QRBBF+MuQMlySgIXQZTYFROysIODClgpx9Z2LYMexbAsBBVf3KSQr7thGVXIaNi4EVzfcfeeAbaI31dFQ0crrD9zMObc9xkuzJxNurOWSh9+hufJ7wo3pC8pgaQ8ioXqWzZzE+Efe5vH1OiP3Ulg49UJOvWZ2ajcw1hrqMsMdCdUDnTuFp14zmyWThjPh8Y/5rC2LmG7jVQR+W+rBn1/GyBseQPX4iLWGUlT3pKBgIqJL4FaDXHjviynaerC0B2fMfJ5WKZscSYD2gH37+xAlZferrGfwP8E0LWQkoqEEa9/cwiHn7s3KJ75LEylxlENtwslifD3nIggGlqCgGQJq7ukIpeOwLQtbN1FLxiGUXoqNhG65UUvHAziZwM3TtukXt4CUjbvPbCzbBivu1NLSSYXflhLfoe6KrTk1abULEbz52KYCgoSYVU5Mk2hrSO87A486BW92gAmL3yPWGuKtRTPYsmollmWhqG4OP2McyCqzLz8TWZGZfO6JYEMYnQAutlaFQBVAsyksyCHpkZj0wjepLGp1a5JJL6xl8ahBSJks+c8Opb3GfNugfNFnzzNr7GQ+mP8NHy+PMvjkufgLZOribcTlYurWraGlJs6sM/vzyGdbuWVYf574dCsjB5ciSRYvT/szOR6ZmG7jFkySkTCqKoBa4GwQCWAjgdGMq+f0TouyDguzuqUpdogg52GZUZLfjwPaldS7yIbb8ShqwUTErFLseAg7FmvXUrgay7JoNbLIkkQkXx7qgMsxUYkkVTBtPFLHxmhmYbgtIgkTqT0gVzRHN6VWiFPsVvCoJoQFNFvGl4hiyCoaOy7Gu8vOXLpZL2R/dROHl8V5t8LDhphTfhZrC6NFs1F9MfzxbwkXj8RbexsAwnYBuSAIyH0OR/v8GayKL5B6HPSjffcMMvhfIUsiTXGDYPu62Iy0YlU+j9LvbOy4je3KQi29GFAdP3JMLKM15Uax7ZrYirelxjN94wuogy5HUHMdQbcNf0XpM8I5fvBU9JhF3h//gtbchGBbmLozrsmBEuysAkyPyDl3LEWzBVp0GUXWu1x/1sdtSnNc3HxSf+59bxOlOS5u/9PeyCJcs9wpUyrNcTFr+CWsr72OCxZdz/LL56HQtQvC9g4gGZX1XzcyAfluhA3UNjZx0Z2LWTp1LOtbWug+/Hralt+WUuTNHn49oaRFwGrFbIshBcsRZAOQwGxpp+msQO12FaJSAIhYRjOCZGFH27BFhdonppGs+IbSK59l5A0PYFsW4cZaRt26mFBUwxUs49w7HueJKeelgt2zpy9kxdzrnQFGEPjjfsUkIk7Wz5sTTA08by2awdnTF6YC9G0/24FtPxNPJInpjlhHTLf5tDrO4WW5ZBeWsfAvncH26DnLicvZTHj869SgtficQZxw91v4JJNmTeC2LyM0fbiGxaMGobbXdS+8YniaWjuuHCSENLqvW7BpjGgkJSlD//250E69atrcxtcv/YsDT98Lt1/BE3Ch0fl7GKZNa9SZnCRJJClAgeBF/+oh5B4nIKi5IOag6WAgEo62ErVlyvM8yNuotCLnoTe+guzfDyQ/dCivFwwHSKP0dlDgnf+3IEh+khV3opZPxNbb1V6VAHVb63lv6f2cPX0hf3/qfo4ceQF5Jd1xZeWy9PaJrH5neao//H3pXBorNlDce18EUQRDp4cfxj3wEvWtYRZMPp/CQDabQiEuun8xW+ubKS8M8NAV51FSXphGaQYnKDdtkH6q3yuDnaKjxryDtl4eLObKE85HzpI48spB1LeEWB9uZMYTC/i8Yg0nDPg9t19+JS5RRXEpTDm2L4IIk47uzZx3NnLb71yY7RRNf/sc0FzbQl70bygDxyMgYgsithBz5gApt115XcFKVqTsAs3mVx0ngjJng+rfqqb3vBlbE0GrRfv6YZT+54GMo5Ww8QWUARPw6mFM3JiWiWDqWKKBzY4Z3Qw6EYkbCO015ELYCay32BEODviBFjBAsyR8ySh6lh/T2HH7uFRqRMJMBeT9gzr5HpMPG3LoAWhaiHB9T/J6xchv+4BNBWOI2B58VgJBDO9wPqnnbxDWvkHy4yV4uh+IIGQEoTLYM+GWFLLzi3GPuJXECzfQ9NajFI24An39Uyj7X+HY+2qVCKLbcReqf8opN+s9O02h3RY8gN45nvU5HSMWwWxqRA4UoQwYTyIaQd57HIYlgdiKlYzS8PRNqbV44cipCP58mvFgJ1vJ8aisr2pi6tL3KAhkceM9L/DYlSNS68/z5ywnmlvELcMKKctxc9vJ/akIxcn3KVzy1Oq0Dfarl69n4nEXM+aRiRiWwb+TB97WASRDU/91IxOQ70aE9QQX3eksvk3L4sZH3+LeC49COGoCWT4/8WgY019ErmxSt/BqJH8exWdfD5IHy2wFpQispFP32q6Q25EJtG0VQcmi6fUFJCu+QQ6U0FC1hfmXncT1L3/HqdfM5uU5Uzjm6gctlwDvAAAgAElEQVR59O/fMenE/Rn34GtEW0NEQvWsmHs9W1atdLLMokShKmJn+QiW9kjLim9ZtZIVc69n5A0PkFfeG9uyWHHvdWkU9m0/owvpQ01Mt7EFkZwe/Rmz8ENEU8MQVQRfkAl/XZU2aNWENS55uXqH52hYNlIXdd2SJxdNlGiM6VzZnmEszXFxz6n7Mv/Df/HuhpDjETliXwpVMROU/4TooF59MP8bmja38cVzGxgydkBaML49TNPCJYlE1G5kDfwLom1gI6KHI9hGgqYnp2A016AESmg7YwbkltIYitC7OBvZVJH9Q9Cqbu3sJ92vQ6t5KJVV1BqW4eo2Ga12QdoxJW+o42+ulmFpFoapEE9qzBh5IACCAGdfeQuNT00h1D55nzp2Cm2NNWxZtZKnpl7IuPlv8OT1o/nTpBk8ef1oRt26mKxAIXde+EeOv3Y+F894lOduvSw1HgBsrW9m3L2P8/ztE1L2WB0ozXEhZ9bRewR2VmOu6RYxIcIR952b9v7Xv/kHFx85kiuW3s7yy+fhxkvcEmmIaFwyOIeGx8en6Yi0Lb8N8YRrsHqMRF89L5XJkfuchphVDoKAnQihNW5AKR2wnajbdLTax8EMpXx9U6rpfe4GG6xwJfq65cglRyD6e6IOvBxt9f1YdR+111ZOp/6FmZjhJvJOuJz6Z6d3LlLPu4u4pyyTGd8JogkDdA1BENAaKhFUD41CnP1y84EW0MEwbNx6klZvUZqgWwdkwaJcbmCT7ohMiYIj7rZio5e+LhXTrCVcFySv11ZUs5lQ/T+pzz2O3uaLiGLzDucTJBm5/9HoXy3D3Pghct8hP/ZjyCCD/wqmaRPw+jC8Pcm/ZAGSbWLKbsQBVxCJG/i8HgQ5r70krXNzUW980dl8x4fo6oZluiCxEUHxI/c8GVvOwmhqQXDlktQVHh7r6ClcOO1+Wp6fTt6wK2lacQ+SP4+8YVciebOxtDiS2092Wz2NS6+mtrmGYKCE+RfczNhFf+eWl75k2sPvIloGmxta0bNKyHEruBQbsGmIaFz+7BpevOTgLjfYA77CTveODK0zg11ApmhxN0I3zdTiu7aplfrWMLWahyohSK3hZpOejcftgWgIo7mGZMU3hN55ChsZve5JxwNCyga1DHffOXj2Xoqr9xxssQiMJPUv3EX4s5eQAyUUjLoTFC8DjzqF2o1rWThxBOHGWiKayYkH9mLB+BN58sYxGMkEy2ZOSgXjo25djCS7MU0LQc3mwnuX8+mLj6WprIcba1E9PhLhNlbcex1/GDVhh5rwT198jAvve5EaO12J3KsIYNvYuoUvWMAaPZ8vI35My95h0GqO6qlayw6U5riQRScy2bauG1cuNQmL7+qjXPnC2rTA/spla/nTwOLU60kvrEUTM037p4RpWgjt1Kth0w/lqEkHIOyCl6ZpWuhxg+aYB02X2Xr/xRhNldS3B+PgBDHxZybjI8kFM5fx9bf1VM29iNon7gJ1HGrZ/bh63g1yCa7uNzplHUo5atlfQC1DLbnUsXbaxqlAUIrRNZt1VXH6jpmJbtlMeuQNrnvqI06+ZApt7y1Ou37kpTv406UOSyRUvYV4azPhxlpirSFC1VtYesMYmmu2UOR3Moxb65sxTCutdrjjeFMkwd2n7ptq+6U5Lmac0A9vZv9oj8HOasyVnVimtcTa2BqqxbAMp4RDgrhmUu6XutQRUWWZFx+6D3nfS8FbhjrockR3PnasBSsSQpADKIW/RU96cfe5F8/eT+LqPQMbFbX0YlzdpmCLKmq3q3D3mYur/HIawh4aY1kY7t5Ifc9D8/akLuHj6TUa2j5/IXbYY8iHzaH+5fnE1rxP7hGjU8F4x33VP34VHmI/zUP+BSKcMMDSyPJ5iddtRvdlgyDQN1vGsgEDhIjz/DSXZ6fn6S7VU6nnY7YvwQ4vi2PZApYnG8OoINwQxLacv2l1f2Oz/2gwQDRrujyf3PdwxJwSEm/PzSiuZ7BHwzRtBFMkZvoIW9mENZWI5qGyxeKJt1dhiUXtY95SXH3udsa8glMQ3b0R1AISUZu6f32H7SlFcOdj6gq1S2+l4bnpxGIxHr7mXLasWsmfLr2elued8U3yZiP58wgOvYymFfdQPX8sjctnQLiBtrcXpI2B2vKbuH7kEN5YuRZN9nHpg69y5u1Pce4tC/i2qpnLn/kGy4agV+H+kfshi3S5jlUlKc29I4MM/hMyGfLdCEWSKC8MsLW+mVlLX2POX85GkkROvanTquzzey/FrydTteXevX9P/TOzyD/xbPTaxY4ns5QLShCtqQUsgaq5w3B1H0DuEaPJHTIKM9ZGXDNYfNUZXDr/DZ64fjTB0h6MmbOcWqWE3mqU5dVbCFVvYcXc6zn1mtl4c4JkF5RiWyam7APTTqmLj5gyD0GACYvfp62hhrbGGl6aPRmAYy+YTH6Pfly2+O9OvYskYiFy2o0Ponhy8YkKbVo8rYZcjyed7KcbfleuAgJx3dohK/jS6lpmj9iXSe0Bdkd2W7WsHTYUNVFk0gvfcMt2CsaQrnjZ8dqw7Az99yfG/0q9EiwjNXl2FcRE4knunngeJWocvbkGo7mGmgVXpd5TMOkFKhrb8K+YguTPI/eI0ajd9kFyFSDKAZT8YWi1C7H1EEr5VLZsbWbK4vc4sF935EQrwrtziTTXkAiUUHD6jZiRJpIV36Su32GLEiztQawtlFbKEaregurxYZmO0Et5YQBpm/GgA+WFAeojBiv+uYWHTx9AIqJjRnVq36yg58i9dvpstldltzICTj8LuqKz333WZGa8siAtG6KYFuW5bkTJ7FJHJB4N85vjhlP1wDiM5hpc3QeQd+z5SNlBRE8uyYYGJH8eoepKAqXdaXr6RoJDz0fJzUfwZZOsme9YnynFqD1vIRz3gWCxsbqBO5a8whfrKwCnvT1/+wRiqKD4sZLNxNa8D7DTfibaBhl0jbakiWkkCOTkEF+zhUbFolwposifRNNceNARW9qwBJG4qoDetQhpN7mefyQHUWMGKZcaKfebdPMbhJoDeGLVmLpMrCUbX7CFAjnJS99+wmF983HRiGA2Y0uBtPMJooRy8Jkk35lL/PU78PzpZgQxMwNm8MuAadqU5uYw9LeDqK7XKPIatL23kOAfzwOzDttyqOrIxTTVNrLo/84lVL2FgUcP56wp91Bw1q3YiDxx8/gUm9Ofm0+kfXwzY20EjrmIhuduSQu+6x6/moLTbiC29v3UvRjNNRRkuykvDFDd0JIaS7fWNxPwSNx8Un/qwknGPLHKWbOeti/zzhjA+Gc6WZsz/9ifXlkuTNFC0zM77RnsGjJpxN0Iv+Jm/qQ/U14Y4Iv1FSx4+X2K83IoL+ycPDfXt4KsUjhyKnKgBMmbTWzN+9Q9fT+2dSxWPItkbRNaQytV947GaKlDDpSQrPiGusevpnr+WJpW3ENzbYVT9y0qHH/DYxw9803q3N2Z+uZmvm82UhntLatWsnDiCJ68fjSy6kLJKU+jI3ZkoQ0pGwSZx645i4UTR7Bl1Uq2rFrJspmTiJgiH7Tm8nEkyAetuXwVyyHpyUeXVGRT57AyN0P7ZnFYmRs7kUxlRZMJk2RYIxlOougms0ekZwXHDelBsUdi8ahBvDL2YBaPGrRTqrnRnmFvixtd7kZ2KF52vO7IsmfwC4IgIwdKMGNtKRXrDsiBEraGTR78tAmfP6fLv39b2cCclz8l75xZmOEm6h6/GrO5ls1Th1G98Aa0+gSS/2JQx2EauVy5+D2+2FDB9SOHEH7murSJuuG5W8g9YnTa+cMtjY7l2ZwXcPuyU2Ug4ATpWjyKbguUFwa498o/8+zqJhZcOybV/8sLA9w98Twe/Wcd724IYWgmn931Beue3sD+w/sQkwSSkoSpyCk1dlORScoycVGkOpykNqKxoTFGRXMso8r+M2BbOvuH1y/ljtOvZMYrC6gPh9KyIaZpUShLxOVsCs6dlabKnnPqTRiCQrf+B6TaXLLiG6oXXUXlnAvQQw1UP3QxNY+MJzu/hIaKjZjhJmoWXEXFrPOpWXQTiCfi7v8kdvc5bG7KYkN1I4ZlMfKGB1MLSHAWkZZhIGkGkm5gtfcxYKf9zBIy+/Q7Q9gw0fU4wWwfeksVEZeLgWo/CvxRTF1Gs2U8kVa0rGwMc+c81TK5AYBKPT917JDSBNV2kGhbCMvSCNflAVDscfPW1y9Q5fodggRZjY8ib2N/2gEprwfK4OEY339E4rU7sa0MTzaDXw5M08Ynqnz18HoijQbx7z+j9tHpaPUJzIiNHrIwNC+JSJhL57/JlBfX8vuzLuPh/zub1rjOUx+vZ+jYG1Nr33g0nBrfWt5/DCWve5cbkHJOuj+5HCihNW7w8DWjmbX0tdTx8sIAuV4VrypyzzubgHZG5vNr8YoCs3/fm+fOHMTs3/em6tnviTcnEczMOjSDXUdm5t3N6JmXx3O3jaemsYWm1gj3Pv0mj1x7Phfd+Shb65tZ8NbXPHDxsdQ/fZNTy+LPTwXcHdk+OVBC/nAnQx3+fAVF586k7olrUnV+WX+awmO3XUmwtAem7OLqdxuobk1y1F4ad526L498uJkL73yO5689fRtBtBfBFfz3tYFdCKmNnvMiWYFCvIlEKgs+uMTD2vokSdNiSHcvsdYYBQV+GlqiOz21aVoUqiKLRw1K817WNROJdjErc+elNrIoUJrjYtEnlUw7sR/TXl2/XQ25I0r377LsGezZ0MUsgqfPIPzhQgpOvzG1my0HSig49y7mfBJhdU2Eia/VMGvknfDstam/5597F7JawKjjvUx/8StGnXAre5fnOyrEXfSvwovnU9/qCCSVBHzoXUzUUlYg9f6CUXfSFEkw8oYHkCUVSzFSTgYdpSD+YBFiVj73XHURcz+qZnVNhIrmIM/fPoHa1jjNcZMHP61jdU2E0hwXbr/K0JsOQvHI1MQtJi5dlcYUcckil/2181iHsmtTVOPmk/rjC7gho5Pwk8M0bRTcuCUP+xS7eOC8qTt4mQPYto0YMYi7AtjtOiLhlkYW3TyBcGMtVy75R5fZczPmqHcbzTUkIy28+di9nHPVTOqfdOYAM9wESh7zXlrD0rc+5faxp9GnrABL6JqRIUudmdI4XgrPu4v6x6+i5f3HKBw5dccacrzQRe3zrx22bRMxLRKxKPFYJZJp0Lv0AFyil4LsKPEWH0nLhScZoi2vEMvaed/ME9twCxqVRj6/az92SEmCT9R8bNvGltYSrutF8T4byXUpWGaCzS1J+gTBUiR8oafRkgdh55yRdl6l7xDQk+irVwAW7hOuRZD+naRUBhnsOejQolnzyiYGj5xB6NnJ1Cy4CjlQQvDcu7AsWHrjmB3UzzfXtdC/RzlLPl3D5Y/+A0tLEAnVETx9Os3PTXWYbqLYteuRrKaOO2uJWTSEVQpzsqhvcdYI5YUBHrl2DB6Pm6kvrmN1dae4YnVrEh2Bza9tIbTZGbt9QTeJsI4rOyOSmcGuQ7B/ReaVTU0RLOvH+boFBX4ioThtLRpCUKGxKcTFM5wgfOXDN7CushavS6UlEuN3vfNoWTQ2RVUMDr0sLfgoOm8WtjeAbeiIkkTMFJBsC8XWqd+8jhfuu4lwYy1nzHwetaQfHlXucEzALQvopo0ASPFmMDUESQV117y1JUmAZCuWqSOpLr5L+EmaAr8pcxPXbTTLZn2jRnPCCXeP6+Mj3hGQN+yoALu70OHdPOmFteT5VMYe3p0eQQ9qu8q6IckkNONnUVnf/rsXFPh3y3l/zPa6PX7s329XIEkiomahNTXhzpZRVBFRgmiLwbqPw/gOL2Xy685GzNF7Bbj56CIU28AUZLbqbnTDxidZSIKNZVs88fpHnHHUwZSLLTQ8ObkzuD9nJm82+hlU5KapNUyvHInokvE7TNS5589DEgRERSVhWpjJBKYtIMsyhg2yYIOhIYgSJiIhy0fYkrBtm4ufWp061+uX/YaK5kTaJtK0E/tR6lL4Yt4qfjvpAC54evUOIm/XHd+Xy59dk3bs6mP6MOmFtZTmuFg4ahCK/r/Ti/fE9rontMf/FU57tlG8MqHqdSzaxuLx/NnLcBX2wB+vp/HJqzvb5uk3EnrjgZRwZ9FFDxHWbXRXgFwiCJZBzLC487kP+ceq77lnwlnk5fjJzclGkCQaGhq58I5FKVX/hZPHUJaXi74NbVKSBDzEHBFF2QWWiWA52fM43t0u6LY7f8vd1Vbhh7XXggI/m6taOHnhZzR/8TpFWd8x5OtXyT1+At5iH5cf/zGxWg96s0T8y1rq+u5D+N8E5ADz24ahCwpX5S5PHZv+psjAbx/ngCGjcQnnMWDY35FVndHvfs5JffK5+qAqNnER5Q2fo0a/htLhtIqDdzi3vu4d9K9fRup2AJ4/TUP05Ozwnh8be3If/qnu7edor3vqc9/V+5IkEdkWEAVQhSjYBhYyupKNqIg0bFrDYxM7k0Yjbl3K9c98BgJMPPN4+pQVkOt1YUZDCIKIV5URbBNBVjEaK9I3IEdOpdrOpS0SZe/yfDbWNjPn5U8Zc8oxFOXnIVk6DeEEccPG4/YQ0Uxmvb2xy7k6V5KoevZ7km0ah5y7N+veq2TwyL1I/g9MlZ/6t9yd7TWDH45Mhnw3wjJtZI9Miy7g8+fy3G3jMQ0T3TT58y2PpN73+l0TyRt+Myy/iWTFN7R++DSFF8wDUSKJzJ0rm1m2al37ont/Zr7zPe9uCDGwJIuxg3vyx5uWUJCTheUJIAgCkmESDPpoaAhj6p11CJbk7/RR2sVFlmnaIGeDDLYksne+i0+r45g2fF7j1Ip3wKsIXbis/jjYWYbd1E10oKDAS0ND8t9m2TPYs2GaFqgi7uJCJEFEty1sCWyXQf26rbhq4tx3cl88hR4UVeKfjTr9gjJVzXGKsmUUyaY2rDHv71u45aS9OP+kPyACcSWLoovng22iWSIzP21jZUUVdw3fB13yUGeI9Dr3LhqeuCo1UavDb+a8e5Zx45g/URDw4PfmUJlIYFogxhKc8n+zdrj/F2ZdxdS/VfDQWQNTx0pzXBhJi6f+WcXVx/Qh2yPTFjd46p9VjOtfRDSUIBrWu9RF8KjSDsc6tBKqW5P8RHs1GfyX6GjPVtJizUtJLl/yEclkEllRwZVFLKnTmPQRPWYa5XlZBLM8tLx2byoYzz93FpsTbhTVxdx3NjHhyF4sWVnJ0L1yGHvqMVx62nGossxd71dy2RHZbGpNMqRbIctuuxzDNJElCY+ipAXjzn3ZRDqEhrTt7zrTqHaGlpiOGXcE04ra6+xtt5/inFYAVENHj+roLg9xUYT/EJCXyY18lNgP0xaQBOe579/TRXydm4amrynP/zPh2gIC3asp9rj4orYFAF00CHsOJcdoRq5ZgVDUC1vOTTu30v9oR4vgs78Se/IyPCdPRSrqt1ufRwYZ/BjYVosmjhtJErGjJh/M/5yjJh9Mja8v4x/7CENL8G1FPdc/s5IvNjhlOn++5RE+fOg6kraE6S2kLWHQ1tDMhLuX8ND/nUs3t5/84ZMRVQ+WFiciZjHpQUdz4/0HphBz5XHWyUN5dm0zpx2QhW7azHq7IsVOA3ZgaM4avg93/m0jTVGNxRcORK+Msu69Sgae1BtDyIynGew6flEB+aZNm7j22mtpaWkhNzeXGTNm0LNnz5/7tjohghE3aFJk8l0CguImZupkSVYalXBrYwtz3v2Si4+dRkG2m4q2BDcufIupF43g7ve3puy77jl1X1ZXfMnYIfuxrj7K6poIt8d0Zo/YF1EVsTVnUfBjBaCmaSElkhxW5kaybQ4p87KyKtYp4FbmxYgn//OJduf9mNZ/pLdn8MtFx2TsbLCEwQKpXb29gwZiCDaKLKKZNlHNZmr75NiB0hwXkiDiCgt88ewGmja38bsL9yW4XyENEZ1zDw0w+jABbIurlm+hujXJ4rP2RjjhVnI8MrVtCW5b9HfqW8IUBPzIqge3BAGPQkw3catql9TgtqSVFih3qKdXvrGFC4eUc81r61KT+IwT+rH5uY0AGGGtSxu0uJbewrfVSsjYpP0y4AhhCiTbDNa92ULusd34v2Vrufe0/RAsnfrWCNcteI2t9c0c2K87148cRcnhF5MfDBCR/HhNeO7LasYO6cHc9zbx7oYQy1c5/tcdmZmLDu9BU8JyxuNEEhkpRVPfPhiHzgzUtv0pYxH5n1FXH8NsVzDvI7pBlLDcQYpzqzAsEdkwEcJJEsEiDOM/M1fKpQYMZGqtIGVSEwCHlmr81dsT97/W0bO8jVBFMYHu1XTL8vDm1nosW8VNHdXe43CbIWRtOd6214gGznL8GreB1P1AXL4g2sdLiD45HteQC1EPHpkRe8vgF4HUOGWCmKVw9JWDES2LfQo8tMUl1jeGuGHJWzvMw5ubkxTYKg9/uIXGqM6U4/vw3G2XIwoCLYkEubleopEoNWG4bdEbfLG+wpnPWzX+svz7VHlYnk/Bo0g8dNZAIkmDpqhGdWuSp/5ZxYNndm66L/6kIkVh1y0bt1/hwNP2wlJA0zOr1Ax2Hb+ogHzq1KmMGjWKU045hRdffJGbbrqJJUuW/Ny3lYIh2Pi8Mn0UiY0xk+4uAQGBf2wK88i1Y1KexM+++xlXjDyeS2Y+mqIWPnLtGNwuN5OP68tVx9iIIjzzyXMsXfkyK654aMfM8E+0gDJNCzOSAEBVJYZ092IDAmDEk2haZsDJ4MdFl+rtcY3flnr4rinJzOH7cs3ydKX+PFnkn29spmlzG76gm+xCL7JhUJKtptqvoOs8ds7+aLaNKgrU2zqj2vtoR5/M9vpYtXQ91auaKB2Ux35n98cW1LT+3CHW9uCndU5tuCzy6tiDidXF2fjcRpJtGvsc153FowZh2hCrjbHxuY2perPaD6uZM2JfJm7jNjDrxP74vEoqUN+2htxRdt0vo5PwS4EocPgl+/Lhw44Q1yNnDkSQRGb+o5bzB+dxz4SzuHLuX/lifQVXLHiNhVPG4FVyMC1QZTjr4HIkAS4+vAfr6qOd2hkj9qXQr+JWJEDYpfG4I9v07vxviIYS+IJuhowdgLQLFoW/dmxqimPGw4iigNJai5jXDd2yKcltI5rIIpcIZtIimu8H4z/3zJSwm1GQCsiLs0y0or2wN35Ha+Q5jMQY9LiLnn4vMcMkafgJCN+z3nUxZfH1+HIPRm7+GNW9Cs27/w7XkPJ64j7u/9C/fJ7k3+ejr3sX9zF/QS7db/c+nAwy2I3Y2Tgl+CQkzcKtSKz4rpm7J57H/815PG3ODgZzcSkiVx7dB8OyCMV0vIqCzyWR43Jh6DptosAVC55L+1xxQZBXxv0GURDQmxN8u+hbsku8FJ3giMRdd3xfPKpEXDOxbZvmuM4972xKBePOJrmAJ9eFJliYGXX1DH4gfjE15E1NTQwdOpSVK1ciSRKmaXLIIYfw5ptvEgwGd/EcP24NeUNDGEkScUsStipjWjZGTCcigmlbyLaBbVkgCDy3uolBRR4CHokCvwtZgU2Nm1ElmWBWDvPeXsoHG75g0QW3Ueot+Y91fT933dCv+fqZGvLdj125H0kSUTwqgiAQ1UwM00ISQG2niv6QLKAkiZiSSFJzXAIUWSJbUXl71pdEQ4nU+3xBN0dNOgBLsonoCXQDKlqSLFhZS1NM5/aT++BXYxS48hyF1e2uL0kiRE3+sc1C4/djByBmyyRsoTNgf3kT+5/Sm7gsIOW6aIhqWJbDDMj3qZTkuGhu2rmI4g/Bnthe97T2+L9CVUSwbDAhbiZZVb+O7oUDeeiDLZx9QCGFWRIiEE6GcbtEgmqnAKeiSiQFpxDJtMC0bSQBWr6sJ6fAh7/Eu8t1ii5R4t3ZX3XZpv+XWsd/h/9fasgnL/2ap19/lYCi8Ye195PVYwDaXsdy3fC3CbUUUhKpp2qziy3eAsxdeJaWDdNaxnCw+3vOyPogdfyzGpXPX3qLwuRWAsHfsc+hB8Ngg0kfr+ZvpxbRPbeFt+SnKU/+i/3EDZjVKxD0Rm79YiBvbXZz+dH7MXRAedq1bNvGqlqF9vVL2NEQyoATcP3+EkRfYPvb2m3Yk/twpob8p8cPua//NE51aAo99MEWhu0dSK2jFZdCQteJJprwe/JpjcfJ9XrJVRViZhzTdmHZAj5FIp5IYBjOXJ+T5cVOGGjtaweHIu/M0f2OLaf7sd0xLLBsG9m2UZMm9cCkZWvTWG+hNys48H+sG/9vn9nuul4GPx9+MRnympoaioqKkNqpeJIkUVhYSE1NzS4H5D8FTNMiAchhixXTPgEg2DObnn/sgeFTyA56cAk2ow4qS2W83YJFzIzRLeD42MqSxMTjRnPV0At3UO7NIIMMHGzL3hCBDj1Tc7t/d8UT3TQtMC1cSCBJYIOl2WkLAsB5bYFuWXglF4aq0c+Twx2n5CAJNrKgIZvBdO9RK/06XVHwO9wGVEnEnaWwtk1j1Yv/4oDhffj22Q0UH16K4lfxeVXcoo0sZizPfknoaA+SJGBJFnuX9sLntrj22D6Yto0g2Ah2jKDq22HM1zUTlwoJUyDepqGHNWo/rGafo7vjylZ+WJ2ixU7bdAY7h2GatIkSZrSF3r0KINKIHCgnL9CKKlu4W1uxBZuWnFLMZOI/nxAQBSiVGtiiF6QdP7hY4+0Bf6Rl45f0b1vNR698xEDtDwCsbxbomatTanxFlXswPfUaau1Dkdpe59r9vyRP6cO0F+N0z8tin5LOunJBEJDK98ddtDfm+nfQ1v4NfcM/cB18BsrAExGzHPs127bBSGJrcQRPdobensHPg/8wTnVoCl17bJ8d1tESBrleP5Kgke+VcSORjJtIqEjYgI1pWKhIqJJEQf6OQe8Oc7RhIrRvqNvtG+qF/XNZdMZAYhHdGZPfrGDgSb0ydeMZ/Nf4xQTkuwN5eVk/6vm33V2KtybxBd1EQwlCm9sIPbgaX+/FjzgAACAASURBVNDN8dcchCfbtcNnc/Ht1uv/HPg1X//HuPaP3V63x8/9+22Pn/t+4m2dfbgDvqAbxSWRne390a5r59ocf81BWIaNpAgccvbeWKaNKAu4s1QE0akV/bmfz/bY3e11T/t+uw87e07/fg6wLZuEJGJmqXQ7oz+SIuDydbaHXcHP1ab3xN/yh7TX11Z9Q01DE7ZpUCw44mpSTiF9ipz/e+NRkoqbCDbiD/g9+ii1vBUfTEJw4xU6NSSuODTODA7l+dBhHN/0Aav/9ncG/e4g3q/SOL4X9OKf1LE/rzWVcv8zHyHZ/Xjm1AbGD/yeAcEA89/6iEfGDUNKVkCy3hFq9e+DIHqgYAT6vocR/udLJD5cTPLDxUj+PGzLxIqHoSO7Jyl4eh6A/7fDyRp07H8VnO+Jv3sH9uR76wo/pL3uqd9tV+/rvx2n/tt19A95XtvPz7mKhJXtoseovdPm592FPfW3zGD3I0NZ303YnlrSqQy5Yw3Mj1Gr93PTlH7N189Q1nc/9oT7+an78A/BnkgDzlDWfxh+ju/4c7TpPbGtwg9rry+trWT+K59Tvf4rzgxUYXz+LDkj7+DsQz6g2NWK0iQSEkv4tu6HLca36EXMCw9nTM7bDFY3pv3NsOCljVm8+K2LY6qexCMmeWfIYD75s8KGhp7c9mY/vtjUhM+tcMcJ+ZT5bfKlCgLmGmTBxLIFxG2ydZaUQyL7CHTvASA4wbUdacCs/Q47GkKQVQTVgyC7QJKxIiGMyq+wWmsRA+Woh5yDsu9xuxyY78l9OENZ/+nxQ+7rpxyn9tTnBRnK+q8Nv5gMeV5eHvvssw8rVqzglFNOYcWKFeyzzz57FF19W+yMmvpzL+QzyCCDXcO2fVgSREzbyvThDH7RyMxLPxyfb91IoKAXjTXL8GfnYG55EVdpf0rFaroVthJZDy6XQDipAvoPOne5XI9bSLI2Wb5DQC6LMGKvCAMDAkv+cTKDKx6lx2dR3j+0nP7FlVTW5zP0oN4cftwpqDRiVb9Po9mDFgr5eN33GEaSkw45CL83SHX9VoLaaoLNL2GEPyLqO4i4ZwCmvxzR3w0BCxEbyTaRbB3BDCNK2Uj7HIdVsxb927dIvD4DbeWTqINHIPc/8ketP8/g143MOJXBrxG/mICc/8fenYdHVZ0PHP+ee+/sySSZkBBi2EQWA8F9adVWxZ9YV0RwrQuoaBWVRcWqKIobioCKVkDAXasWl6pVW8QFW22rVTYXkM0YwpLJNvvc5ffHZCYZMiAoMAk5n+fpUzKJc8/MOefe+95zznuASZMmcdNNN/HYY4/h9XqZMmVKtou0XRmzQ0uS1G602oZNkto5eV3acXHDYG2dwF+/jlijnwN7FKB8tZojjx/Mr/f9N2AnaNsHLxtojGvsbECuCosBtjX8L9qLoZ5/siLWjeWxbhzk+J4Kx3osCzZ5yuj1mzL099bSe9NHrF7Tk+P71fP0hW5CkTh1ga9o6Hwk1d1Pp7D6Q4hsoUtxP8a/uYm5Kzbi1DbzQ10M2IeT9s1n3K/D9NDfJa/+XUyhEbJ1IWjfhwZHT1zxjZQ0foJqRTHVfCJ5JxAvrUDp0h+z+mviX/+dyPsPw6KZKEW9UAu7I/K6oOR0QuQUJv4/rwTFlbc7qkPqQOR5Supo2lVA3qtXL15++eVsF0OSJEmSpL1Y3DB4dclyiruUM+upWWiK4LjNb3Dk0eUUl9Tg6etmS30ZPmsjtUY+gfBP7z2eyVHOZXwe68Md/vMIWU5UDP4b7c3RrhU0Gi6+ivUEwHXMdRz51lKqX/2Y8G8PIt++gsrlpxGP+9EK/o1+8AFs7HYa7pqVFNuXM3mwxYtLQpiai2N+XY63fjWr35nHtEXrKC4pYMjQcnr0c+J0bcJtVlEc/Sdm3MHmnCOpdfalrOF9cv2vEIusIpx/EmqXctQu5ViNGzF+XIpZ+yNG5VeY37wPVnrEJNwFxLvsh+EtQ+nUA6WwJ2qnHginnBIrSZKUSbsKyH+pnUm20hbfXx6/7R5/dxx7T3+ebNff1mR5tm9vL09b+3y7Q0f4jNA2P2emMtX+GOC/a9aSW/M/gpEA9totrP3xISba19FnvziF/RyQ78PTQ0U3NJz+WhyqwZpIIYYR+lnl2Eer4Sz3x/wntj8HOb/kCMdy/ho8ksXh/qgYnJHzGQfavmFu48l8fty9DPzHWBbMXcoF1/Rm//7P8P2yQiIb7ah/exk6eTDzNeotgwKPzpWHRYjWbiD03WzQIxx0bC6+bgPo2UshNzdELNrI2lVhHM48SsoEmjdKJ/1TbNEathRVEI7nUVT3P5QN3+LXDiIqilDCArRCTJ+PmLOCWMhAD0VQzDBOewM5njrcrjoczlWoyjfgt8APrASEgiVsmEoOpuLFVPNB82IpKiBACIQQJPbNEIAFmImA39JRzACK2YhiNqBYIUzhxhS5mMKNYToS28rqMTB0LFMHPY5l6GDEwIhjGXFQFDbbHZhCA82eWDuvOZr+DaoIJ45jNaIIHUSyLICiYdkLsRydsOydsByFmLl9MPMG/Ky63xk704faYn8DWa6foy2XTdq12k1SN0mSJEmSpN3JNE3iuo6um0TjccKhCIFQkGgogBWux0aMeKwRp0NHU3RUYqBHMK1du4978ja85Q2agkBRBJqmodidqFo+CA8IDSzR/B8JmgJbK7GVmWFgGHFMQwczjsBAUXVsmoWCjqIoCJsNoToBOwg7YEegIBSBME2EzQOKHSGag2fLtLAMC8u0mhKOWSjCQggDIQzARFEFCBWhaghFRSgaaA5QnQjNDooNEAhFaSpzZpZlJQJzMw5mDMvUsSwLITRQbSA0LEBYViIwtwywrMT/sJq/SAEIkfoMKCpCKFhCJF5uOoZlJL6n5DefmjctVIRqB8UOipqole2UW5IkaUfIgFySJEmSJEmSJEmSskDJdgEkSZIkSZIkSZIkqSOSAbkkSZIkSZIkSZIkZYEMyCVJkiRJkiRJkiQpC2RALkmSJEmSJEmSJElZIANySZIkSZIkSZIkScoCGZBLkiRJkiRJkiRJUhbIgFySJEmSJEmSJEmSskAG5JIkSZIkSZIkSZKUBVq2C7An1dQEME1rt7x3QYGb2trQbnlvefy2ffytj11UlLtL3nd3ttetZbv+tibLs327sjxtsb22te97d+gInxHaZluFnWuvbbWu2mq5QJYNstNe2+r3Lsu18/Z02XZle5V2nhwh30U0TZXH76DHz/Zn3xXa2meQ5dm+tlaeXW1v/3zQMT4j7B2fs61+hrZaLpBly5a2+tlkuXZeWy6btOvJgFySJEmSJEmSJEmSskAG5JIkSZIkSZIkSZKUBTIglyRJkiRJkiRJkqQs6FBJ3doKm10hYATQTR1N0chRc4jHTABUVUGzBJiAArqwMAwzuwWWpCaZ2q5pINusJP1MW5/zsUGD3pjx+iC1by3rWlEFQSVEWI/IepYkSerg2lVAvmjRIh566CEsy8KyLEaPHs2JJ56Y7WLtFJtdYX1DJZfNn0ilv5oyXwlPjJhMN28ZpgFW0GDRrGUE/RE8PidHXzEA1aPKAEfKukxt97lR91MY98k2K0k/g6oqrc75vx5Vzm3vPcw7yz5Ouz7IYK19y1TXAy/qwbVv3sXGQI2sZ0mSpA6s3UxZtyyLG2+8kfvvv5/XX3+d+++/nwkTJmCa7eviFTACqYAGoNJfzWXzJxIwAmiWYHHTxRog6I+weNayxBN1ScqyTG231t/Ix7LNStLPkumc/8/ZKxh52FlA+vVBat8y1fWSp9cyYdDlsp6lDknEv0cNf5LtYkhSm9BuAnIARVFobGwEoLGxkeLiYhSlXX0EdFNPBTRJlf5qdFMHk9TFOinojySmMkpSlmVqu15bjmyzkvRzbeOc38ldkPo5dX2Q2refqGtZz1JH49o8BkftfWA2ZrsokpR17WbKuhCCGTNmcNVVV+F2uwkGg8yePTvbxdppmqJR5itJC2zKfCVoigYWeHzOtIu2x+dMPDaRAY6UZZnabkM8INusJP1cSuZz/pZQbern1PXByEYBpV3mJ+pa1rPUoVhW6p+qsR5D6Z/FwkhS9gnLatEr2jBd17nsssu45pprOOSQQ/j8888ZP348b731Fh6PJ9vF22G6rrNiw/et1pCXd+mFqqjUVQX46PGlqTVmv7mygvzSHIQipwBL2ZWp7T436n466YWyzUrSz2CZVqtz/q9HlTPhvQfS1pCXd+mFprWb5+dSBpnqOrGGfHJqDbmsZ6mjsOK1hFacCYC97EZshSdnuUSSlF3tJiBfunQpEyZM4O2330699rvf/Y4pU6YwcODAHXqPmpoAprl7Pm5RUS6bN+/YtJvdkWV9Z46/O3Tk42997KKi3F3yvruzvW5tR7+/PZVlPdvtaWt7c3naYntta9/37pD8jHt7lvW22FZh59rrrvoMuzrLelvuJ7Js2WmvbfV737pcIr4e1+arAdC9FxHLGd4mytWW7Omy7cr2Ku28dvMotqSkhOrqalavXs2+++7L999/T01NDd26dct20XZaPGbiwI0DwIB4i+DFMMzmGWvt9x5M2kttq+3KNitJP0+rc36UbV4fpPYtra4NUAw7HuyynqUOR5iBFv+uy2JJJKltaDcBeVFREZMmTeK6665DiMRU2HvuuYf8/Pwsl+yX2d5ouSR1JC37Qqw2hMcu+4LUscnrQ8cg61nqaITVYkcBsyF7BZGkNqLdBOQAp59+Oqeffnq2i7HLOJwK/mgdmxv8bAnU8tK/32Hs4IvlXqRSh2OzK2yObGZ9TTVuu5NQLEK3whKKnEWyL0gdks2usL6hksvmT6Q418fYwZfQs6gMh2bDo8iAbW8h61nqkJoyq1vCiTDrs1wYScq+dhWQ701sdoV19T9y6bxbUwmyHjx3AtPffYrJQ6/DgTvbRZSkPSZKmM0NtfzxlWmp/jDjvD/ideagJCbvSlKHEjACqSBtwimXM/7FKWmJQOWD272DrGepIxLJgFztJEfIJYl2tg/53iRgBFLBOCT2IB3/4hTOPvwkuRep1OHEDJ0xL9yb1h/GvHAvMUP2Balj0k2dSn81Vw86PxWkQaJvXDZ/IgEj8BPvILUHsp6ljkiYASwEluJDGG0zqZok7UkyIM+S5EW4pUp/NZ1yChJ7kUpSB2KYRsb+YJpyU16pY9IUjTJfCflub8a+IR/c7h1kPUsdkhkA4cZS3GCFsl0aSco6GZBnSfIi3FKZr4Qir48cNSdLpZKk7LCpmfuDpsqHU1LHlKPm8MSIyYRikcx9Qz643SvIepY6ImGFQXGBcIEZQtAudmCWpN1GBuRZkrwIJy/AZb4S5o68C58jX64Xkzocj5LD3JF3teoPHkU+nJI6pnjMpJu3jAH77NeqbzwxYrJ8cLuXkPUsdUhWBIQjkdQNE6xwtkskSVklH71mSfIivGD0I2lbnUQjMhiXOp54zKRr7j6p/mBTNZlhWOrw4jETBUda35DbYu19ZD1LHY2wIljCAcKZ+NkMYakymbHUccmAPIviMRMH7kQOaQPihrzwSh1Xy/5QVJDL5s0y0YskgbxWdBSynqUOw4qC4sASLiC5L3mn7JZJkrJIBuRtkKoqaJYAE1BAFxaGvDBLbZhss5K0a8i+1P7JOpSk7RNWGIQXlMQIOVYwuwWSpCyTAXkbo6oKVtBg0axlBP0RPD4nR18xANWjygu61CbJNitJu4bsS+2frENJ2gFmFLQWI+SmDMiljk0mdWtjNEuwuOlCDhD0R1g8a1niabsktUGyzUrSriH7Uvsn61CSdoAVAWFPrSHHbMhueSQpy2RA3taYpC7kSUF/JDH1TZLaItlmJWnXkH2p/ZN1KEk/KZHUzZYaIVfMQJZLJEnZJQPytkYBj8+Z9pLH55Q1JbVdss1K0q4h+1L7J+tQkn5a07ZnCEfiZxmQSx2cvETsYTa7QlQNERQNRNUQDmf6z9jg6CsGpC7oyfVnurCyXHJJat1+bXYFXVi/uM1u/b66ru+ujyBJbda2+lJQBIiqQeJaKNXvpLZpZ86HP3U/IOtZ2itZcQQGFjYQamL7M5nUTergZFK3PchmV1jfUMll8ydS6a+mzFfCEyMmM/3dp3h32eLUzz3yunLc2ANlhlapTdlW++3mLcP0qD+7zW7vfeUevFJHYhgmalNfEhbErDg3vD6Fd5Z9TJmvhAfPncC8j/7C2MEXy/7RRrWsw+2dD3f0fkDWs7TXsaIACGFP/CxcCFNucyp1bPLx6x4UMAKpiy9Apb+ay+ZP5OzDT0r7uUFvJGoaRDGImoYMxqU2YVvtN2AEMAzzZ7fZ7b2vJHU0yb5UrzTyuz9dzjvLPgYS/WL8i1M4+/CTZP9o43bkfLij9wOynqW9jbDCAFhNAbklnDLLutThyRHyPUg3dYpzfdwxZDT5bi91oQYeXfg8+W5v6m8q/dXopo4ji+WUpEwUQau2+8W6Fb+4veqmnropTZL9QOrottXf8t1e2T/2Asnz3sHdy7l60Pmpei4tKE79jaxnaa9kNiU9FLam/3eBDMilDk4G5HuQy+bg5lOvYMwL96amqM0474+YVvPT8zJfCZqigZHFgkrSVmx2herGem5/bWaq7Sanz2qKik1Vfva0Sk3RKPOVpAXlsh9IHYnNrhAwAuimjqZoeG05VNdn7m91oQbZP/YCmqIxeMDRjPzNWYx/cUqqnmdfcgcHdy/ni3UrZD1Le6emKetWUwhiCSeKFYRYHY5VD2MUHIre5eRsllCS9jg5ZX0PMkwzFYxD4un3mBfuxWVPJH9JrhnLUXOyWUxJaiVgBLh03q1pbXf8i1O49fQrmbjgYdY3VP7sBEQ5ag5PjJhMma8EkP1A6liSa4mHzryGX991HkNnXoM/WrfN/vbSv9+R/WMv4LXlcPuQq1PBOCTqedSTt3P1oPPleVDaawlrqxFyJTFCrjYsQav7HMeaWWDJRMZSxyJHyPegmBHPODW30JPHP299AU3RyFFzZAIXqc3Z1rTyulAj7y5bzPKqVSwY/QgO3Dv93vGYSTdvGQtGP5IaISzxFlJbG95VxZekNivTWuLNDf6M/U1TVCYPvU5eJ/YCDfEAdaGGjPW8f2kvFox+RNaztHdKBeTNa8gxQyixqtSfCL0Oy1aQjdJJUlbIEfI9SFXU1ChgUpmvBEVR8VheHIZbXnylNik5rbylMl8JmxpqgOa1jj9XPGbiMNypfqBp8lmh1DFketi1JVCbsb+piiavE3sJ3dTZULc5Yz1rsp6lvVirEXLhRFhhlHBl6m/U0LoslEySskcG5HuQXdWYcd4f06bmzjjvj9hVGXxIbVumaeUPnjuBRxc+n/pZU2Q7lqSdlelh10v/foe5I++Syzj2Ypqi8dK/3+HBcyek1fPckXfJepb2bk1ryE2SI+QuAETkB0xPdwCU0PrslE2SskTeQe9BLsVFma+E5654AEUo1ARqKfB4ceAijnwSLrVNyYRTea4cFox+GEUoxIw4d7z2aCrxUDJYiMst+iRppyQfdiWnrQ8ecDS3D7kagUj1N9Oy5PTlvUyOmsPYwRcz/d2nuGPIaLr5uuB152KaJgEjQI5d1re0dxJNAXlyhDwVkBt1WJ4BWKFKRHRztoonSVkhA/I9xGZXWFtXmbrpSj4J7+wuIhppvuiqqoJmCTABBXRhyX3IpaxJJpxq2W6fGDGZfQu6MmPozXAGoILQFKJRmQpYknaGqioouqCnqyt/Hz2PqBpjY+MWzn5sbFp/6+Ytk8HZXiaZO+O+YeNxmg5M3WJl9Vomv/MYGwM1st6lvVfLKesWoCQSGwszgKW5sTQvIlaTvfJJUhbIKeu7iaoK4mqEkAgQVyMZE/dcOu9WGuKBFv+NghU0WDT9S968/VMWTf8SK2igqrKapOzI1G5nvPs00VqdD6Yt4a3b/80H05YQr9d3ezvduk+pqtitx5Ok3ckyrdT5/q+3fcoH05agNmhMf+eptP522fyJBIxG2d7bsJ97bjIN0BptfDBtCX+b9B82PN/IjFNuoXNOYVO9B376TSSpvUmOkFvpI+SIOJbqxrLlImK1WSqcJGWHjPR2A7tNwTIMgnVhfvhxA7cumLHNLNW6mQhkHIqKzRBE6mM4vYl1NUF/hMWzliVGzCUpCzK125GHncXiWcsI+hNPuXdVO032Awcq4YZoWoCvqoKq0AaGzLyaX911DkNmXk1VaMNuDVK2Vx5J+qUigVirfrTsrTXcd8p43r/6KV4aMYNDuvWn0l9NVI+n2nvLdulQVNkus6BlHThVDX/c/7POTZolWPbWGg4e1ptBYw/i4GG9qVxUw4RBl//iRJmS1FYJK4qFDURTHxGJEXIUQHU2jZD7s1Y+ScoGOWV9F1NVBb3RYPGs5QT9ETw+J7dc+AcUoVDmK0kLbsp8Jbg0BzQaLGq6MfP4nBzx+3589cZqatY2JG7W5Iw1KUuSCadattvOuZ1Y71+V9nct22lyzXlyC7MdWfuamh3Soh8cfcUAVI+KYZhECDNy3i1pI4cj593Ca6MfxYYz7b1+zvF3tjyS9HMl26c9KlLBOEBhDy99j+3Kvx79JtXmZlx4C3d//CcM02DkvFt489rHURs12S6zKNO5YeBFPeicU0ilvzrjuWlb5yRFCPoe25XPnv0m7fofdoebE2XKlUDS3saKguLAIrHXeGqEXAFLdSI0LwRWZrGAkrTnyYB8F9Mswf+annjbPRqxoM7qRVXse1pn5o68i0vn3ZpI3FNxDLedcT0RXaMhEMPhtRP0Rwj6I3z27DccPKw3H89eisfnTDw1lPdaUhZsnXCqzFeCLzcPj8+ZFkwk26lNy7zm/KfWQmqWSN3gQvOo+3FjD0zcjwqLO4aMJt/tpS7UwKMLn+eLdSvQTR1bi/fZ1pr3rY//U7kafqo8qqoQUxR0C2wC3CZYliVzPkjb1bJ9TjvjprR+VH5i91RgBok298OiGu459xbilsVjF03BgYMv31qVdn1Z9tYaDj67D4a8SOwRNhTen/UVTq89VQ8IeOziKcRME0VYVNWuRYhEsLG9cxIGrer8s2e/4dej9+eJEZPx2nKIyvOJtJcRZgSEo8ULzSPkluIAzYMwowgzmvhZkjoAGZDvYtt64p3jdGFhcu+wcXTKLUBVO3PVS99SVR+lNM/BlGF98H6yAd+BRageG5rXTunAQipO6YnedGGXpD0tHjPpkV/GS1dNZ3ODny2BWh775/NcOeo8/jl7RdoonS4sQhnWnF82fyILRj+CA/e2D2SSFuBD4mdhJpaAbAzUc/trM1M3tA+eO4F5H/2l1QhSpjXvWx9/h0a/t1UeIcBhY2MozrgFy5r770l98L+3nopTeu7S0UqZ5HHv0rJ9Tlk4hxkX3sKyZ9YT9Edw5trS2pyvh5dOJ3VnxPNLU+1sxtBOVJy2L+HaxBpMRRP0H9wDYSXaimwbu4+qKtiFghm3cHrtHHD6vnz27Dc4vHbKztmPG/78daqe7jqlJ8FYBJ/NnVbnh3Trz4RBl+OKuxCGhWVYGc8zdtXG658tZMQxZ23/vClJ7ZEVTQTkVtO9rXBgIRCKBYoTS/MkXtYbsewyIJc6BhmQ7waZnngfO/oAGhpDXDj7RuZfNoMZHyWCcYCq+igT3vmOWedUcMWfm2++pg8tTwy96eBQVHlTLmVFQzyQyvqctGZzJdPH/RFhKKk2CZBjuXn6vClsCdUyZeEcPl+/PLUWcruXVYWMo+4NG0M48+xMe/fJtCB7/ItTePmq6ThxYdA8EhWMbztXQ/L4Pzkav43ylA4spCqms8kf4573VrXqv9OP2bf1+/wC23twILVPLXMyfL5+OWPeupsJwy/nmC4D0A09rc3tN2Rfrnk7/ToxZsEKnjx/IKv+upofl9Tg8Tk58qL9sSwLDSFnN+8myQR89fVhAAac3CN1ne9/UT+ueSu9nm59aw33ntoFty2cqvNDuvVnximJBzDr/avw+Jwcf92BGc97325ew6E9B/z0eVOS2iMrCsLe/LMQgA2UGKbqQqiJh1BCbwB7p+yUUZL2sHaVDSYajXL77bdz4oknctpppzFx4sRsF6kVM8MTb4fXTtgmyC8sZf5lM9inoCR18U6qqo9SE46nXdTHLliBbtll5nUpqzIldntn2cfUmvVEMYiaiTDAChp8OH0pX0xfQ83LEWaccguHdOvfvBZye8cQFkdfMSAx9R1SM0uWvb2WxbOWMfKws9L+vtJfjUDBMNKnhX6/6QfKfCVpf9vq+NsY/W454zdTefqd3YcxC1bgsqsZ+6/qse3SnA+aJVg8axkOr52D/1BB+chy/IE4NkX2/fYqmZMh6fP1yxn3+n3UUseUD+Yw8KIeqTZn9zlT7ayiNJfpQ8uZfGpfDKB8cA8Ke3gJ+iN8+vTXiTYnn9HuNskEfA6vjbBdgVIPfc/pja+HF1uBI+P5oDC3E4LmOr/z5GtTsyEgcc754i8rW51nBlzYjcnvPEannIKfPG9KUnskrAiWsAMtZn9aWiqpG8mAPN6QlfJJUja0q7P9Aw88gMPh4N1330UIwZYtW7JdpNa2Glnz9fCyz/D9GNFi5PuBIW6O6+1j0crmLJKleQ5qg/G0t6qqj6Kb8OlPjeZJ0m6UKbHb1gmHMo06L3tmPRPPvwpnrj2RxGg7szoMw8SeqzFo7EEE/RFiQT2V2BCgPLcs7e+3Pn5yWmhxro8Hz53A+BenpK3XTDv+NkbjW+ZqMAwT1aNy3NgDwQSbQ6XGMKmqj9IQ1inNS78JL81zYATjuzbng5l4mFc6rBdj3/muxbTlcnpbchlLe5Sj5qTlEkkuv7jrjcf5/a9PR/fGOH7cgRiGSVATlOY5KPTYuea3PZj0dnMbuP93fRl4xr4seuhLgv4IpmkhbEIG5buJqZs4vHY2a4Jx/1iZttTMYVMyng+ECW7hwlIFz426n2LRLO56DAAAIABJREFUia/9G9Le98clNfQ8tZgjRvelrrGBLaFaxrx1NxsDNRR5fT953pSkdqkpqVv6a00BubA1T1mP1+35sklSlrSbgDwYDPLaa6/x4YcfJtZxAp06tb2pLLqwOHb0AUQaYjhz7UScCpe8sCRt5PuG175l1nkVfLspSFV9lON6+xg/qBf14TjTh5Yz79MfWFrVSGmeA0389GieJO1OmRK7tQpytzHq3LfoACw7O5TlXBhQvyHIf1/8rlWw7MvN46QBxzDysLPonNsJX24emqIRazp+chS/0l/NlLfmpBLAlflKyFVz046fHP1evNVU8K1zNRiGmXro5fW60erClOY5mPfpD0w6uQ+T3v6OQo+dK47qRrcCF0ZdlGPHHIAQAgfqz15eklw3riDodVpPrm0KxqF52vIzFx20U+8ptQ3xmEmhJy8tQeGUt+YgLMEhnfsjTIGJienQsQnBn86pwLKgsi5MocdOVX2UqvooN/7tW+adXQEk+oeqKcSEvCjsLoqqJPria4l14hWluYw8siuGXcESgqln7s/1rzavIb/zlL7EayMoeU4sA7rYO1NfFcz4IHB9zQZKS4u56IUJqfPr3JF30cmVTzgk61Ta+wgrCiIn7TXLUhCKAKFgyRFyqQNqNwH5Dz/8QH5+PjNnzuSzzz7D4/Fw3XXXceihh+7wexQW5vz0H/0CRUW5WKZFXVWAT5/+mqA/wuE3HpJxOpuCxY3He+jXeQB1YYNRTUH7cb193HVqXwJRnTyXDa9NyXgRtzlUvF53q+NnU0c+/u449u5ur1vb3mfI8fTi1WseQTd0NFWjc24hmtZ8+gjVRzO3U7uGO2/HVkEG/WGWvb2WI37fLy0p4jFXDMCb5+aBwRMSQbR3E+6T3eQW28hx2HHm2onVhxhccQznHnEWBR4ftUE/sz98mbuGXkt+nqfVsax8ixNvPARTt1A0gTPHnrgZ2I7iPCcPDevPda8s55EP13L3aX1x2VTGLVjRnPfhzHKqX19FVdP63t9cWUGn0pyffO9UuZrOH4seX4rTa6f/yPKM54+4YdIly/1ta7u6vWb7fLK7/Fgb4uV/v8vIw86ip7srU4dMwOstZkujjt4Yo/qTKgae0ZNaxWTMX5rb1qST+/DIh2tZWtWYmD1lWU39owJ3voMcre0uZWiLdbmj7dUyLRo2h3AVuVLB+NYzFqYNLefu0/piWqAIKPbYiYV0Qppg9Ztr2O/XpRnPbUdd3p8b/34/d511XVrizGnvPMnYwRdT3qVX2nk2k7b43SbJsu06O3N+baufLVmu0JYYit2FO9+V+l1kswqqIM/rwrJsxBC41BB5e+CztNXvC9p22aRdS1hW+5j7uHz5coYOHcrUqVM57bTT+Oqrr7jyyiv5+9//Tk7Ojp2oamoCmObu+bhFRbls3tyIQ1FZ9WElvX5VilAEYXv6CDkkprM9dcFAomYcVdi49Pkl27zQTx9aTmcEH81ckjaaJ7bK5Jw8fiZ7IlPz9o6/J2Tz+Fsfe1edQHdne93azn5/W7epuBajYWOIJU+vTdub113sQInbf/L9IJG4cNH0L3F67ZSf2B27R0OPGtS56yjJLWLxQ8vTshu37A/2Ahs/NMYY/+o3aX2nq8uWWKZmNSVmMi0UVYAi0DGJxXds4Ufy+1FVBUNTiZsWILj0+a9a9e0nzxuIK2YSqovy7aIfOGh479Q6+x39DpIPNg4fcyDX/GMlhR47I4/sitelEY4Z9O+SixGK/sS77Zi22F6zfT7ZVVRVwYYChpV4KKMAGoRro/xz9ormDN1vfZv2UKfQbeOi51q3rRsG9WLsghWU5jmYf24FRlWI/LIcQvH4tguRZbuyLnflzemOttdkn+x/UT+u+cdKbvtdH+7823et6mbu+QegYFEb1hn3avpDuq5uG0bEAAtiER0jbqJHDSL5YfIdXmxC45tNq5n8zmN8vn45kFiWs2D0IziMbWdZb8v9RJYtO+21rX7vLcvlqr4IyzmQmGtw6vf2DdNQ1BoiefeCZeL85m7MomOI7Hv1HitXW7OnyyaD/+xqNyPkXbp0QdM0Tj31VAAOOOAACgoKWLNmDRUVFVkuXTNVVeh+SAkfPJoIoEsHFjJjaDljWoyiTT1zf2pCOuNe/ZrJp/ZNXdhHHtk1FYxDc2K3+ecPTK1lbZnRumXmdWsbJ+od2uJJknZC5jbVH90b4/Cre2OYJnbFRiASxmHYMXdwO6aWU8k/nr00leDoptceZNqQPxL0Rzh4WO9WuxgsnrWMI68/KBWMQ4u+c04FtkAcPWqmZq14fE5+dUk5do+G3aXucFCeVBOKM3bBirS+m1RVH6WhNsqn81Zw5EX7039wDxSxY6PjQKup/6teW81D5/ej3rC4rUXQNuOs/hTZ5DZXbZmqKhA0eH/WV6l2d+RF++PxOVNbBmbK0D321RU8cf7AjG3L69ISeUhO7suPH1TS55gyzLiJQ1Hl7hu7S1Of3PxZNY8NG4BQRca6qWqIEI4ZrXZgGPvqCuYOG8AHd3yWOFdePoAfl26m56+74IzbWfxQ83n0icvvYaOyiR9rN/LowudllnVp72RFsYQt7SVhAjaAxPXSUt0QD+zxoklStrSbgNzn83HEEUfwySefcPTRR7NmzRpqamro3r17touWxjItFs9pTm5VtaSG/uf24eYT98NlV4kbFvkuG3XhOE+cPxDTgpnD+zPrk/V4XVrGC71hQVgVqHZB3ATdtNCEYPX76/nuH5WpaYuuHI2YZabdlO3QFk+StBMyt6nlFA53cu9/5nDf/13Pv+Z8vVMPgFIjiU7BoHEHgWWhmwZra38EYGPjFvYZWIi3s5uivvkcPLg7liqwCYjUx4hZcMOgXqn8C9A0tVsITJfGd39blVbefz25gkPP7UNeaevp7NtjaCqb/REmn9qXArdtm8ndktmvDz23D848O6qqEFOURN9VBHYzvZ8mf99owWHXHMD3f12Df20D/rUN9Ima3PbuVuvI/7Kc+ecPRJUBWJuVqZ98+vTXHDv6AIL+CL4eXtzFLiaf2peGsJ5qu1X1UWKGlda2hgws5pIjuqKpCvMuOIA8G0QO68L7TUnd5IPW3agpCWRZRRHxxhgBp5qx3xflJGYCFXrs3DCoF16XlqpX06bg6+HFv7aBxXOWMWjcQQhFULclwJEX708sqLPivXV8OudrCoc7uf31mcw474+4bA6IZeuDS9JusvW2Z4BlghAWwtKxEKA6EYYMyKWOo90E5AB33HEHN998M1OmTEHTNO6//368Xm+2i5XGyrDtWSRuMvrl5VSU5jLh//YlqpuEYibXv7okLQmMaVqZb/BNiz9/WcXg8s5c32Iq3INnltMjqLP2X9V8PGtp881/y5uyHdjiSZJ2yrYSuLm7MPKws1LBePL1xbOWcey4gWhOhYZ4AN3U0RQtkRQuZm5zJFG1KdS/H2XGKbfwUfW/Of3kEzAwyT+hK6NeWUahx851x/bktr81jxy3XGdbmudg5eYgDyz8nikn9SHSEMfflLU96I+gOVQso/XMkkxLPJKvbwzFUyNgx/X2MfXM8rQ+OX1IObULf0g/BrApZjJ2wbK06fTF9sQIt6oqrX5///C+8PIqog0x7PmZt1XSTQu5I3kb1qKfFPbwppZhqDaFvieUYTuwKLVcqWXbrQnGqG6IMvnUvkx881sO757H2Qfvw1UvLUs791ur/PJB6x6gC4vfXFmBHjVQczRMRXDnKX3TZqxMG1rOuys2clpFl8Q5qcXv7jylL5sCUUqH9YJXvse/tgEBRBvjqQSWyW0ev3pjNZ3cBVT6qxnzwr28es0j7NiCH0lqJ6w4AqNp27MWUrM8Q4AHS3Eh9OCeLp0kZU27Csi7du3KM888k+1ibJdQBf1OKEutIbdMi4a4SWmeg5FHdiVuWAhIXbAhcXN921vfMu+CA5g+tJyxLaa3TxtSzrRFq7nm2J6MbrohS/43419dwdzzB+I7sAj/l5tRStzUBuLkKgo2Elmid2SLJ0naKdtoU3k5Odg1G+v936X9edAfoa6xgXqlsVWm9m7eMhQ980jicdccQN/juvLZM98wbOxJvD/9Sw6//iDGNwUxNwzq1aofTXr7O24Y1IsHFn6fCnCq6qNMeOc75lxaQbwhRmGODUxQ7SpBBWIGaIpAVQSGBboFDeF4Ymp+1MTlsbElECWmKIxb0NwHk9sWPnb2ADRFIfhjgOo3VtPryFLW/Ksaj8+JHjUIKaT6dLKcyaUoqmESU5RUMJ78/Y1/+5YnRx0AWMQtmHvBQEzTQlEEDWGdN5ZWoykCGX21TaqqoCD4v/EHY+gmQhF8+lTzrJEjxh3EiBeXtGq7N5+4HwVuG0/8cz3nH7oPN5+4H72KPFzaYj156tx/wQEcNaATqiKI10b4dsFqeU7fDQzDpFNpDsG6CHEFrnl+SWoUvLPXTp5TQxEKp1Z04cf6SMZr+22/68Mt73zH3IsHsPLVVVjAx7OWpp3zPnv2Gw49tw82r8LB3cv5Yt0KdMOQAbm0d7GaHi5vPWVdTwTkgiAWHizVhRLbvKdLJ0lZ03bTsrZTmibocWhiDfmbd3zGB48uIVdRmDG0nNJ8Bw5NpS6ib3PE6+3lG/nTORU8d/GBzD5vIDlCsGilH1VkXrcWMyzGfrya/BO6MuWD1Zz1wldc8vwSNjWNPCbX5Xp8ToBtbvEkSTsqU5sacGE3bnlrBh63O/V6ksfnxG63pYJxgEp/NZfNn0jAaMQyzYwj7tFAHJtLw+m1p0YbdZNUP9jWEo/exR5uGNQrNVKefL06GOOqv33LukCcZe+tY3VdhIuf+4pTZv2XEc8v4Yf6KJuDUUY+/xVnPPk/Rr2yjE26waZQjAuf/pKqhmir4y1a6UcRAlcgxhd/WkrVkhrsHq15vXAnJ3GLbfZ3SCxByfT7qGlyyfNLOOXx/zDxzW8RQjBj0RoeWPg9VxzdHafsw21SMsfCwmn/4+8PfoERN1PBOCTacaAhlrHO98l38vL/qjjv0H2waQr/+HYzxjbah2FanP7E51z+56VssSn0uqBPYi9yaZcTikBXLYymvry0qjExFd2y2NAQ49LnvyKmJ56GZKorW9O682jcYMDJPYmH9IznvJwiJ2uDPzLhlMsZPOBoVEUhqoaw2eWtmrR3EMmAnPSAnKb+I6ymUXHVBXKEXOpA2tUIeXtg6PDx7ObRPofXxsZNdbiLXSiqygMf/8DpFSUZp6arimD4QV3QIrW4zTjVm0xKOndJTFu3tjGd3bJSIyY3DOrFopV+Ct026rdspDBfQ1VtaLn5aUnhLDWx57P2C/ZKljoGm10hYGSYZu5ROXbcQOoaG3HZHQTjYUYedhZzPn2JK684j09mLU+NBg64sBubdX8qGE+q9FdTH2pEVdWMI+6RxjhfvLKSQ8/tg1AFHp8TpwqzTyvFoxrkiUaO713A+ytrAajoksPlR5RgRsPk20zGHubFIdzUxgQLVkZpCOsUeuzUGQb7ntaTy55PH6G85a/fcPOJ+6W9NvHNb1OvmabFzOH9cdnV1NrQmmAMoy5KuFFPldvjczJo7EGgQMwy0QQZ+25yhFtTRMbfr/OHW420JbNsj0uOsO+GOpd+ma3XjmsOtVXwpTfGMtb52powry3ZxPdbwkw+pQ+jftUVe7SWvwzvgtNuI6JbbGiM858tCooRY86w3jRETZ77TyVnHdgFb44Ny6ZtM1eB9PNFLIHSoi9f+5seuIVBTI8x/jdlqMJCxWL2aaXk2y08zkR91YQMLCMxS87m1Fg840sGjTko4znPECa5TjeNdUFuP+NqdNPgjtceZezgi+nmLSMek3UptXPbHCFPXEOF1QgCLNUJRhAsC3YmMaoktVPysesuZhrNo32+Hrl0PrmYi2fO51dX3s1ZNz/C8PJcPly1hUkn96G0aX/m5PpBTViYG1cye8RR3P27nrx67fFEq79j6pB+vP7VBqaeWZ7230w9s5ynP0usV01m4K3oksNNB5i8M/4E7vpdTx655Cgaq1eiC5MoBrqw0Bt1Fk3/kjdv/5RF07/EChqJdbyS1ILNrrC+oZKJCx7i66rv2VC3ibp4HQ5nYu1zkBB6xOBfj37Dv+77lpqXI5zX71SsXINjxw3ksOv3o3C4kzFv3c2PtRsp85Wkvf/gAUcTN3VueWsGR43qnzbifsTv+7HivXUE/RFyi1x898EP/PbqCqJV3/LO+BOYc3Y/5l12DGP6Gxzfu4CKLjlcfUQhE6bP45oHn8FR+z1vjh3EnLP78f6NJzKmv8GqTY1c89se3PPeKjZkGO2uqo/isqsZX6sozUVRBPe8t4pLn1vCAwu/57pje/LwGfvj1RRWvLcuNfskJkzCpk5Y1zEME7tpMn1oet+dPrQcu5m4uc70+2lDy5n1yfpWZfG6tNS/9T20JZ60k7bKsRAL6q1mjVR/UsW0Ifun1/mZ5byxNPHQqiYYwzRNzE2reOziX/PImX2Ye/mxmJtW8+3cGzmrtJHb5rzC0BumcuvD8xlenovPYyNqWIx4fklq1kdyppT0y+mmRThuMPXMco7r7cNphrjg9sc4Y/wD3PrwfBobGugUWs8740/gsbOa62vp7OvJC6zlgTP6oSpNM31iBkdfPqDVOW/pgjUUG0WUeDvx9YbvueO1Rxn5m7OY/u5TBGSCK2lvYCXOjVtnWUdPrL8SVtM2X6oLYZkIK/1hpiTtreQI+S4mFJF68r3vKWVc9NBcKjclRvAqN9UyfsYzPHvHVURRmXn2AMIxA5dd5ZEP1nDL0T6eHDMEf9U6APxV65h33RlcPm8xv9q3EIcmUpnZdcPkyc9+4LUlm4DEDV1DWOeKg3L4y40npr3H3OuGcM2Tn4DmlVnXpR0WMAK8/sVCrjvxQkY9eXtq7ffckXfRNXcf7Iab/zyTnr182TPrOW7cATSKAGquQjQSwxIWL/37HZ4YMTltDfmtp1/JeY9fT6W/mmt/cyHHjj6AWDBOpDHOV2+spmZtQ1NfivLNPyop7qsyd1x6/3hyzBBunreYsOrh7NsepXJTLfdedAKv3Hxuq7+7fN5ivg+bqQzpFx9eyoFl+alsyG8srSYcS+8FpXkOCt027jylDz/WRSj02BNbHDWNWM///QG4LPjViPJtzjYxDJNiu8L88wemRi49gBW30BQVHavV71VFUBOMtSpLQ1hP/VuuIW+jtsqxsOK9dRx50f5p2+71O7YraxZW8vBx+5HT2YUlBIZlMfb4fZnwf/uxanOQuppNvDnmjLR2/MLtl3LmjdN5csyZ/H7ii7z72YrUdeW5O65inT++zVwF0i+jKYIN/ij/WVfL9b8tY9gtM9Ou7bWbfuTlicMy1tcL1w/lzIffB0cJHp8Tza6y+tMqBo05iHB9NO2cV1cZTGVanzPiTqLxGJf9dpjcAk3aKzRPWW/x8NuywIhhWQKsxIMnS3Ul/l4PYtlde7iUkrTnyUfnu5DdpqKogl9dUk7pwEK0Alvqgp1UuamWmG6yoaaRuvpGcjWDz1b7ufyo7ph6NHUxT/JXrSMUjqAImPT2Sk7+038IxnQ2BWL8e109kD66UmC3Mr6HacQTP8is69IOUgSc/6tTU8E4JKaZT3/nKYRhoRgKBw/rTWGP5p0Ogv4Ihm4y8S8PU1W1iW7OUp76/RSmnn0DPfLLWDD6Ef556wssGP0wjZFg6n0nvvkQDZEApmHxxSsrU8H4kRfvz5K/rgbAMvWMbTviDxKuC6f6mi/HkfHvVFPnL19uoK4hSDAQ4KyKQt5YWp0a8b78qO5097lazUJ56IM1nDnnc+55bxXX/LYHFaW5QCLgicRNNsUNdNUiahrbnB5sGCZqXMeNha0xzvtT/5c2QwVAjes4DAM1rqPqRqtR8ztP6cu8T3+gNM/BQ8P6p0bYpbZl6xwLkYYYbp+dQeMO4tRJR3LouX346o3VbPq2Dl+ODX8gzqUvLOHUWf9l+vuriUXD5ChxStwiYzt25/nwV63Dl9McnlVuqkUTVsZZFXImxS9nmRaOiEGJy8bg8mJicSPt2n5wn270KMrbbn2VeBS8msKgsQdi6Aal5Z2wLIsv/rIKgAPP7MUxoypweu2Ueoup9Fdz+fzbaIyEyHV6ElugSVJ7l2kNuWUgLAuwp0bILSVx/pRbn0kdhRwh34VUU4AKdo9Gn6H7sbKmnrLigrQL9+Aj+hOPRnjhr+8x/LjDMGM5HNUtF7dbQ2+04SvtnnZR95V2x+FwMOmvzdmjG8I6Anj83AocmoLVNGJ+46BeOCK1Gd9DUWygKmBtP+t6pi2f5BrEjsm0LHTDYNoZN9HJXcCWUC2vLf8HfzjkfD6YtqTVdj3JIPrH+o3ccswfWPbMetb7VzUnEvSAw3DjAGyqgu42KPOVUOmv5vP1y3non09x6+A/MGjsQVimRTQQ5/OXVwJwzKgKPAXRjG1bKBrOHDuv3juamvoAUUPP+HcbQybDy3MZP2M+lZtqKSsu4MExF7IlEGPphgDXv7qCyaf2Ze75B6DHdDSHRkMkzukVJWwJxlla1ZjK4j52wQpK8xz8UBsGwNXJvUOjkDs6QyXTqLqqCO47rS+aIijOc1KzRd6otEWGkcixkMzbodoUwg1Rlr+9lv6DewCJ4EuPGhi5Nq5/fgmFbhsTT+hGZ4/G9z9UM+PP7zFh2FEZ23Go3p94PdC85KKsuABFVTPOqpAzKX65SCDGRzOX4PDaOWBUf77bEEhd288ddBh/GHo8wgxvt75U1cY/pn7BkRftjyPHxrcf/MBh5/blwCG90mZPHHnR/uTkO3lpxAymLJxDvjuXy+ZPlFugSXsFYTbde7acsm42nbcse2qEnBYj5JLUEciAfBeyDAvTgg8fXcLBVw/Epgqeu/0K1lVvYcaf32NTXSMTR5zO5PlvMOKUY7h+5p9TgcGsGy9hud9k5EOvM++6xDRFX2l3zp+6gEbNS1X9Ggo8Nu4f0o+obqXtT9tyH+RHh/Vv9R4jZrzGmk8D9P5tQWr0ZnFTUNAy63oyO/CirX6Xtq+51GHYVTtO3cU3L29gvX8NHp+TW675Ax890nq7noOH9eaLV1Zy1BX9qQxvYNkz67cbdMZjJj5nPnNH3sWl826lc04hVx/6ez6cvrS57Y0aQNF+XkrLO/HZs9/g8GpcfP8CnrpxaKptXzrjNSIeD7+/8/FUX3r8+gs5974/8+JN56T+buSM17Dy8xk18bFWS0ieuf1KakNxGqImLptCzDAJmxbjmraa2np/c69LS+wVPqQfgahJF68DE7Cryk/3k52YoWIYJqphJib2NX1xjqZ/KzLJTZu19UNNwzT4ZPZyDh7Wm0/mLk/bm3zA5f05vHseQ/vlcMWUuak2PHX0Obz24X+55J4XeOXm81Lt+Lw75vLR848wcsZrNDg68cRNl/Dyov8w8oxBhE2N+8/oh78hiEsThHWLbp28icRu2f1K2j1TT+SGSdady+ni6YmjqAsEcdltXHDHbIrzcrl3G/V18fRXiahuBlzUi6/fX0/vo/dh3yNLMU0rFYxD85aPh1/Qj5qXIzx80US0XEFxro+4ocuAXGr/mkbIW64hF8mAHDvCbBohbwrIiTfuydJJUtbIgHwXUlQF0zBxeG3Uh+sZN7V5JG7OTZeQn5OLbugMP+6wVDAOicDgivuf5JW7RxOK2Dlh8ivk2FUCMYNYTheCkUSG1uIcOxsaEpmfq+qjXHRE11Z7k1/9ynKeumAgJz34DzyqQdBQacwrwjfQABMM0kdvWo6COxRVri+XgERQYTdcLJz1v7T2oAeMjAFl/j4ejht7IGE1jCfuJuivavU3Wwed0YhJ59xCnh11P0W2Qj6anh7oL569jEFjDmLhjEQZivuWkN+pKxff93dUzcTQFeydihl2yyNpfenKqc/w0uSrGDZzET67yeqNtVw970Mmjjw94xKSuoZGhv5xJmXFBcyeMALNkc+4rfYNb7m/eXGOgztO7kNUt7jzb9+lgvbpQ8spcalELLHtLNfb2MM9OUNFat8yPdQ87toDCfojuH0OHF47fc/pjeqx4fHYcDkElx1ewvCt1iNfP/PPTBp5Bn988X1mzf2YhlAEj9OGbgp+d8ND3PbsP3j3sxWph7kL10UY5DWIBeuZMP3p1HVn7k0jyMvPy/K30v4pmoLH56S4bz4R3URTBdF4nEAowrXTn6NyUy2Vm2r544swYfIr7Nu5ALvNRmPMalFfz1JWXMCfrr0QV74DI2pgxDJv+ahogqA/wpKn19Lr4iLGDr4ETdHkTAep/UsmdWs5Zd2INb3mTATkQgE1MWVdkVPWpQ5CBuS7kFATI1flF/RkY0Mjk0aewWML3ueL79Zz+X1PMmXsSHoWuijMy2m1/uy+S0+gxBkkqpqYeV5swqJniRNTMQkIeGx4fyzLolNTUilgm3uThwyLUX9tDohK82qYc04FihA4DBWspiAcMz0IaGPry+X0+T1PVRXsQsGImmCzOHhYb+wejVhQZ8V764g0xtlnYCH7Hlmaen31p1WgCrAs3LoLd56LfQbW8uOSmtT7enxOTMUgKkKprdMgsdTi97Nv5OnzpuD02jhmZBm5Pg1L2IjrbkwzUYbKpZvpf2J3Fj3yVVobPXRMc186uHc3xp5xBL4cB1o8QFC4ufTuOanf19QHWi0hKSsuIC/HxcF9uvHFd+sZNWU+L991FXOG9yGHKKFIlA1Bkxmf1lLgsTH1zHKmvf89p1eU8MDC79OC9scXr+PKo7szdsGKtCC92N48cr69GSpS+6dZgv+9tSat34TrouwzsBBngZ0eF/VNtY/jevu44ujuhIKRjA+K8nPcbKpvxHTmkuvy4iWMahms39KAL9fDe1NG0qMoFwuL7nm5RBQYcufTFOfnMmnkGeTnuNlY20BRfg5Cpov5RZw5do6+YgCGR2NdY4xQKMSE6fN54saLeejy31Hed4hqAAAgAElEQVTkdbK5IcLdLy/mnHte4N1pYymyWTgUndUb/PhyEvXVvdiLiUbYMnG77EQDesbzqctr55hRFax4bx15Di/ufFfivCmvf1I7JzJte5YcIRdOsDYgaDlC3rBHyydJ2SID8l3IMi3W+P1cNmV+2tTD+599my++W49LE0z76EfGHF2SCgwO7tONp8YMwrnsLmLfbERxdWa/Qyex+c3ZbF7+IVpBF5xD7yJsK2bD4lp6nNIjtQ/qtvYmtymC6UPLmffpDyytakz8XhF8u+gHXD3caDkqOXlO3A478XjbHL2T0+f3vOR3Xl8fxuZUUW0qX7yyMvX9H3VpfzSnQsXJPfl4dnO9HDNqAJZpsmjGVzi8NnqeVErPM8roc2Y3qLX47oMf6HZ8ERc+dSMbAzU8MWIyPfK6Qhwchoe//WEO9dF6fnuxF+urCcTDGxGuzigDbmfJQoNOh5bQ9eQehITA4bWntU89YFBWXEBxXi53n30YC249NzVd9JLpr1Kcl5sKdh5b8D5TR5+TtlRk6uhzmPLs29z4+5NT/bS+IUiR6ad+wa3otRvoVNCFB8+eAjk2NE1hwv/th25arR6GnV5Rkgq2IHOW663XF8sHTXsXRQj6HtuVz579Jq3fHHpeX/y6ldY+Tq8oYdyCFUw6sSdPT7wMt8NOXSDEYwveZ1NdI6FojEfGX4ShGzgaKqlpao/e/r/l3vNHEf/vJKyma4babxyWnssxFb0447eHprXxJ266hNL8fAxDPvT5uYQiEB4VXVG47a1vueP/ulGcn0sPR5CahZPQazdQUtCFWSPvZN6n68mNbuZPfzgTf9U6Ko4fwt233YfxeaK+NFdn1P43U6uWYnOr9Dy9jNCWCMtfXUu0Ic4xowYQCycC8wOH9MKT46BBBOQe5NLeoWVA3nRKSk5Zt4QTBRNEFBQHFgKhyxFyqWOQj813oUY9mgrGoXnq4dRrzmHwEf1piJosWunH4/Yw56YRlBUXMPGCY3AuuxsrvBEAK7yR+H8nkX/EaQDotRuILLiVQP0mOh1cQGhLhMfPqWDm8P4s/GZTxr3JZ3+yjgcWfp/KCF2a50CJ6Ch9HIx8/EmOv3EqQyc+yo/+OlS1eS3q1tmBszl6p1kiNYoIzdPnNUuund1dkt+55lBx5tpZPKf5+3d67Rhxk2BNNBWMQ6JePp69jMbqMA6vjc4nF3PxzPn8+g/3cObER9loNFJxek8e/e+zfL5+OZX+ama8+zRmyKBxQ4hYOE7U0tinwI311aS0fsCyO6g4rYhrF63ilNn/ZcSLS9hn+H74mrK6F/bw0rk0nyduGsGE4Uex4Nbz07c6G3smE4Yflfp8X3y3nvlvfcwrd13Fq/eOZtLIM7j/2bd597PlXD/zz1w19HjKigvo6bMRaQp+INEHwy9NwOavwRGz8OgmdkGq3yUVeGwZZ6wYFml7QRuGSdQ0iGJsNzO7lD2qKoirEUIiQFyNpJ0nk78LiwBRNURcC6f9TTIYhxb9xoItwVha+/C6NArdNtAj3DzrLwy79TEmzXudmy48hWcnXk5hYRGm6qSmujKtPeYfcVoiGG/RV7RvphGvr+Ta049stRzqsvueJJLcZUP62QzDxLQSD+L+n73zDnCiXLv4b0rqbrZk+1KVpjTFLmJBuIqIdFR671KlKSKgIF1AEOlFEK+CiFiueu0duzQbKCBs382WZJNMpnx/ZDdL2OC1oJ9izj+7SSYzk5m3zHmf5zmn1K+zaNBNFD4xOWycUHbfz8TbLmfT+C6hsejGXkOCZPyU+2U/+BBxZj+dp6+k5aiH6L9yE2ntU7HEmXh37QH87gCNbqjFN28cBwQkMfqoFsW5AcHwYyCAcYrtmVZZQ24PbqN7qtLWo4Q8in8IoqP8WURA1yOmHpa4vYzr8S/ePVpGZryFgCGw4XMX22ePpFndtNBEXQnDm4sUW1X3p7qyibfJmBLNjHntezqu/ZSHXj3MtfWTSZEE1t3ejBdGXM6q25vy9Ocn2b0vL1T3Ovya2izr2hh3kZuRj2wNe1AbPH8Tfk0NHUfTdCSHxBXjGnPt/RdxxbjGSI7/p4j0Xyx9/h+BimuueFQMI/z6N76pDh89/jWyRYp4X2SLRO22GdXa2Io9r+M2FEZe348N/RdwSe3GjL2uL/5SlZzviiiQRRa+8yO6FojYD8p8vrCI85T/fEu9284jqW4clw1rSrZhsP5zF3XTnBEth+qlO6mZmggE09OHdWlLQDPocs9KhszfzOffHQ+da1J8LIvvuoPS0rLQQ3YlVFc2NodIQNHJzfPicwd4uHP4YliizVSNpGfGWyjPKcfwaGGkPIq/LiRJIKs8m84rR3P1nDvovHI0WeXZSJIQ9tlVc+6g68oxHMn/iem7lpJVno2hGWGibRd3qcc3bxxHUXVcnkBY+whoBkOvTGfcw8E08/XTBrBsbE98SgBNNFHoB8HQiLfJYe1Rio2P2FfssQ4sohFxDlKjiz6/G5IkYpJFMuMtvHu0jJpOR8RxwtDC7RnjnMkR75dJ1Fk2tifrpw0gNcHByEe2UrttRmg83bvtm6Dwm6bjC4Sr50cRxd8Whg8EM5wiTCrowQVDQ6wg5CHrM1s0Qh7FPwbRlPWzCJMoRqxRLSxxs+PNT5g9pAuDLktF0vwMbxFHiuhFFGUMWzgpF2xpaO6S0Gs5MYMir0qp7g8jJxN3HWLptefz+WP7uXzSJXR/al/Y+WSV+KkTbyXJJHHSRMQHtYCqYbGYgmRcEjhRVMyQ+ZtOSXccSGZC/J+f7vgXSp8/1yFJQVsmURSIcVo59OoxWg5oHHb9zTFyiKxHui+qX0Oyi9W0EQbeei23z65SQF88diznJdXkyx1HaNSjAQOf2s/kNvX4saic8yL0g1xP1c1unhHL+KsSqZXip+awOhQLBi6PQoEnwA/Fka3OTroN5owdyPlJVn4q9mOy2zEEI2I/TUlw8FNuETZbDEpiRtjDtpyYgSib+ckdYMqbh0M1wGvuaIYgCuiGwcYPjzOrfUNmvVQl9PZwl8Yc2foth0qVqDjiXwiSJODDS0BXMYkyVmyhMc6Hl0Ebp3OiKAeAE0U5DNo4nRfGrsaim7EoFh7uNI0Fr6/js+MHufvfC5jd+S4GbZzOa+M2cfmYi8AiERNj4utnD9OodS00wyDVYWbNnc0o8CgkWCVSJS+yw+ClBwZQLtr47qc8Hnr8BfKKy1g7dQBpcXFkF/oxqSrOU9qj5i5BiNBXyt1liOa0iG3bJEloJvnMYoNR/E+YbGby3QFWd29IIKDi033IEcYJ1RBodmNnbugxCEdCMvaE5Ij36/usIrrftyWstE2yB8XjFI+Kp8iH1WEi11OILc4cFXSL4pyAYHhBsGFwyjOlXqm8HlPxRoWyumSDqO1ZFP8QREM2ZxGxkpn1FanoQGiife2Tgwy89Vq6T3+UMQ9vhYKj2HeMJu/hzhQ+uwjTZbMQbGlAcKI2XTaL4r3PA4RqyOOS01m3N4dmGbGsbJ/OE53TuPeaROwJQSMU7bToCwSjc/4CH4ZhEBtvDZ1XJWqmJuLN94XSwH1aIETGL2lYm1mDOuH1K3i1QFjK5p+Bv1L6/LmMyrrxVxd+xvsbDnJVvwvxlSoceOUorYZWXX/Vr4XI+pV9Lgi7L5cMqIceq4FihLWxUV1vDEufTU1woPgFigN+6nesheLTyCrxE2eTmfd2AeXNZob1A/mKB9nyZXBibp4Ry6JrLSTvHsvJhR3JWz0IoeAHdn2ZxZjr6/LaCZ07Fj2DM7MOECTj3ebvZOnHJSx55wRHSw3G7j5MzXgLdqOc3ff15Kl7e3JJg9pB5eNJ/cgpLGHyqqfpt2Qn1q5zkBMzgKo+eKJcJyDBgx0asbRrYwo8AYY/tZ+jheUcyS/n42MlrHj7KJPb1GND7+bce1N9rJ4ARUdLscSZ8UoCiiSjm2QsFhmLKGFBwiJK0ej5n4ifi4ADBHQ1RMYrkRabhFQm8dbD+/hk8WEKd/hYdut0Lq3dhBNFOSTY40h1JJHl1Rj75mG6P7WPwc8cILldHXyJFobuOEDvLV8y/N/7scoCaf4sCtcO5uSiTpRtHolUdJztr37I4tG3s25sZ6TyQlIoZV+uF0dSelh7LN77fLU5Q71gIoo5heV7PmTxXXeEzUEbpg3Eh8zA7fu4dc2nDNy+jzxFj7a5XwlBgECZizvue5QbRs9n6qb/ktx7Udg44bj9ITyind6TH8L03lrcW8dQ8p9VmC+fHXa/hEtnI5oTQ9HxSSufYvwdN4FicFX/Czn06jFinFYscSbcggdRiN6rKM4RGD4QrZzKx6mMkAuxAIgVXuSGZI1GyKP4x0AwDOMfw3AKC93o+h/zc1NSHLiLvJR7VHwWldzCYgpL3Lz+6UGmdm1JeXk52S4PsXYbsc9PRXIkkXB9fyR7HAgi5tRMDF3FH9D5Kd+LSTBIi7cjyCZKDTMGEv/+Kp+bnMU8M617lb/y8uewxZ3H96+fILZVJlP+820oOrfwlkbUspnQzSCKcLKomMGnRL8fG9uX3JfyaDmwCX40vJpCyxEPcUnD2kzp0/40YaCfj5SnpDjIz/95v0izSULSBQzNQJAENNFACZx52f/XqKz/kuP/L0iSAP4SNE1Bksxg+WWZAacfOyXF8bvOoxJ/ZHuthEWUeHPpl+HeyO3rEpdmR5AFBKHifgngLVN4f91BrHFmmravS0yKlWxPPj7Jx4r/bmPMtX0xybEMWbCZE3kunp13F13uWQkQsU2tmzKADV8W07FZOs/vz2b4pYnUdUqATpbbICHWSb5XY/yuQ8y5MZXk3WOrRaMKOj/CfW/ksb5XcyTBQPYVowcUFGRe+jHAtfWTcJh0VFXDapZR846yYVynMH9yS2pdUDzYRA10nWOFbra8dYBerS7kwlrJGIJMviLy+MsfclOrK3BrMokxJhJtJjZ9dJzbmqWz7M0fGXN93bDo+IJ2DcnaeQSAGj3qh/XNpV0ak/PcD2TtKwwtNgm/QrDwbLT3U/d1NnA22+vZ/H2nIyD56LxydBjprulMZ/ddj2LSrBimAEezThJniqWg3MWC19cxo90osreXVesn9mQzfgIosp+AYWHF29l0uCCROItIqV/nhW9cTLjxfPLdCqVelY0f/cTUlonEPTMm1JYttZuS2GYIYko9RMGg3JVPYfZx3tqxkZtHziS+1gV4fH7i8KKrCsfyS9ny1gEG/qsZ56U6kCQTX/9UxIzNr/H598e5s83ljOjSGlmS0HWduBgbc187StcGFhLNBi5FYNf3fqa2rY8UUCNeo7OJv2JbhV/XXlNSHOQUldB52orQgvX0Hq2onRJHUrwDHQHdgFLDRJzhRXDno7ldFL+9Bf/xA9ibXE9S50mIgo6mC3xzwsWMza+RV1JW4Tn/GeNuvwlDM/C7FEq/LaPhtTUxBIPjrixSUpyIgeou5H9kP/m9iJ7b/097/ate98rzshTORtQL8ccNo5KVy9lvYcp6DX/D/piUTejWNgSkNph++jeSUkj5Jav/8PP6K+LPPrez2V6j+PWIpqyfRQgyWGQRyWTDbC5n55uf8GCXFngeHx1SYU3ps5CyepfjaNGO/J0PorqysTe+nqQO41E1AU2QWLzrPfJcZUzreysTV2wJEZjd9/djzeDuYcJVG8d1Yvjm92nSrQHl5eWs7lofSZKwmMyouT5M8SJ+dAIBndqJiWy5ayCYBbRyneMvZeMvDYTSwGUpmHJ/emQzKAy0iV1zR2P6jU3GbJJQS1XeOkWdu9Wwppjj5DOSck3Tq7L0/uDsSkkSKM87zIZxnUNkbfDy3dhT65/b6sSn1eoXHi3l7VX76DD7KgKGjuHWQuJ6NZoncd2YZiDAseKT5LkMEuxxpFgSWdJ5Gj5DxQCemTsaXdMRpaoSjkhtaujCzTw9ZzTP7C9kzlVmCraN5KQrGzkxg/S+S/AKycRaBDb2vohEtYiTEeo1k63B0gxXeQCPojHrpZ9CpPfRHk3wlJUwa/MrDG17EU1rJ5NfkosjOZ2irGPB/jO+M2O2vItQcpKi3XNRXdk4EjOY0m0ug9e9ydJxPVE1nR1vfMhtrVqQmmhn0FNfh46xuEtj/GqwlZolkTV3NkcSwSQJHP/vcYqOlnL5mIsYW0HGoUJ9/dlguUnWvsKQYGE0pf3PQaQI+ImiHFRdxSqJUCaTvb2Mw0X5xDitPNJvBs6keA4X7QWCZPyijueHKak37VsbPV6nR2MHdy+rWvRcMr4vsgDL3vyR5BgTczo0IsVwkVPRlmMv60jiDf3RfW5EXaHwpUcoP/Q2psQMugy/h2cfm02HyctR7UlMf6MguP9HdnMiz8W7+w6zZHxfXv2hhE6N4sgrKeOShrXpesNl9HlgXegcts0YyqhGAbZPuq1qbJu/EzGqj/mr4FfUEBlfM+g62LsNx6Ud0EhEciTjFmOJ9+aSt3USasU4ltJ9BkWvrKL84Ns4249nbu9rQ/dg7pztTH/6Eza9+C5ju/+LHjNWVfnHTx3IZ898H1qwyxyeBjFitMwgir8/DC+GYCEsRK4HgkJvogxYEXQ3SARF3bRoynoU/wxECflZhF/VsdolxIBO/cxUHhl+K/lrh4SpsOZvm0LGoEco/M9KkjpMRE5IA8Mge90oJEcSye2GsWb0TaiimSXPfMi8fm1xxloocvspLSuLKFzl9njJ9+Yx9NTa7ykD8B8sJ6ZVTURNB6EMv18hJdnE/j35nDwlMleZBm6VTKyfNhCvXzmjMJBJ4ldDkgR0XzFet5tmHZP47q0Sio66eW/tAdpMbPHbLvbZhr8kRMYheF03jOvMmM3vgxz3/3xyfyB+plZfNoSQ7RzAyX2FFJ/w0HLcBcQTx/G38sm4wcaBl44R3yohJOhWmVFR0xlUQB8yfxMJsfbIGgaaztAW8RSsHRTeT7bejWPwOrK9VryKxqVOOWK9ZoEvWJphM0tM3h0kyl2bpzCxZQJ2uQS/DIt6t6J0+93kVDwk95/+MFvmTuTYvr0UZR3DJgnkVZDxyuOXPzOd2b3mcCwn6KXe75ZrmL3xOWYO6hxGrCc9e4h1vZozuc35TNvzTYioP3BrI5Kvq8FtN9SiWNMjqq9LMVU+rFHBwj8PJlGmpjO9WoRcFEVMiLyx5qswF4F9jx+lzcQW1GiexMl9hTS+qU6YkrqnyMeBrce5ZNyF3L0sXNTw7mVb2Tn3LlbemolsqPxYlIdPlrElZiA5koi/qhvZG+4KI3CauxD/8QO498zjhh7DKHS5SLTFM+1f9ZEEgxceGIgeUBBNZh4/6GX3/jxGtWzB0w+OxCxJHPnhMGtG3UqR289LXxwlxeZHU3WGLN6C16uDKKK48zAHauOWYqM15b8QkiRRMzWR6T1awd5txF9zR2hRXU7MIK3vInTFi+RIQnIkkdR2AFKMlfSeM8l/YSWabjB81UuIkoSuaezdvYkJnbrhl2MYvmhLuODqgk1sHDEgtGD3bnTBLopzBILhBSn8mUrQFRCDGSCGYINKUbfKGnLDCBOBiyKKcxHRwqSzCNkQUP06z9//ET8dLkYwtIgqrJisxF9zB4UvPIxanEvuE9OQHEmkdRmDcWQF/jf7oX8wjkkdGnFkxwI29r2Edx68kzibOVQjWwlnZh1sFkuIjENFRHvhZjJbpaJ4FFw537Gif0sevOU8Vgy8hno3wG1zrqL1hIvD0mQ1zSAzIZ7M5ISI9ebyb6g5rIw8P9K/JYvuaMTWGTfTsI2Is24sniIfxl8k+qxpSsTFDv0ctwv62Vr9MyjdmwUz+x4/yvlXZbJ32zekXpFUTV19yPxNlAcUMhPi2TV3NDVTEyO2qYAhoqlKxH5S7vUx+Il97Poyi5KSIuI6Tw+r14y5YyHbDriZ1b4hvoAWIuPTr5aQP5mI/42+CJ9MItbkQXIkhfbr3jOPjiOnA8H+Y5zh+A0ynCx76lXsFjOqqjPw1msRT3soyCrxYwQvFZPb1KNZpoOsEj/3v/gtAEUlfjw55RH1HTRPVdsKCRZG8YdCkgTKA+Us63kPNZ3pQJCML+91LzN2LUdV1Yht3uvy0+SWutRonhQSODx9G0Or7rKRmuAg0Z9Lyfoh5C7uRNwzY0imjJjeS0lsM4TcJ6aFL0TtfJCE6/uHXscnp5NX4kUJqOSVa7hPfMuj/a9hbvvzebT/NXTLdLPgtgbkF5cwe+Nu/Hk/8NqM7mzsewlHdixgbq+LMX8+Feu+KTizV5OWHsvzy6ayY84oSvNOMG3PN9Ga8l8Ii9nC+mkDyEiMwXFphxAZh+C9yt06GcPvIbnjJNK6j4dja1A/Hk/g03tJ6TCMgGBizaj2zL3tAtaMak+Ldndwfo00kuJjIy5WSvaq+xFdsIvinIHuBcEa/p6mgFSxQC3YECpqyBFtCIYWEn2LIopzGdEI+dmEDpIcVEk1xVvQRSNiVA8ITeaSPQ7VlU1ql7tRDywO8yrVPpvNgDmPovsfAUHE6/UyYvXLrB7RLpT21nvJLgRr5Ald0VRkq4/Hp3U7LfLbiTGb30eLEPnVNIMYkzkU2Tw14mmVTL8+fTtC5Hnrvd0Zse5dvn6pCEES/hLqsZJsjqjSLUqmv8Lp/WHQNB0pRuKmKZcS8GthtfqyKBHjtGKNM9P4pjqYY2QEAQRdDBLzCmJyuro6VGVUaBjYzWZUQWH9tAEMmb+5qk1NHYhLNeGUI0e/Ux1WPhpWHwSBJ2aPorQgm44jp+NISMbrKUNyJDGpTQzekiLSJJFt3WtzfoqFwMcTw/qRun8hSW2Hk7VxEhB8eE5MrcGE9a+Qfl4jNF07g1qyQUpiLOV+BVXXmLTyKXbOGRX2OzPjLRwtLOeuHQfJjLcwq31DVrx9lP1ZZRT7VKxWCdd7WSxo15CpL39XrYYcgmT82qhg4Z8CH156rZlMqsPJ7M53kWCPo1zxYTdbGXx5dyByxoivLMDnO7+nzcSLMYzI26h6oJrC+exeN1CwbVIYcfM8NQW91yriUupGXAiS7MFxWU7MICYpnW3PvsqM+g2pIRSzckLVWOpITscozaN1vWSOFpQxp/9NrB14bejzW4aMR/tsdjW/8rtW7WLnkhlsmtCVSSveQtMNYpVCbJYYvJLt3C7R+R0wiwYpCfHIioEkJ0a8d6LZBkop6tcrwq574NNZxFz9MJO3vIFugMvloqwwl4zkDFSzKaIyvlZexcCjDiNRnCsQDF9Fyvop0BUQg4TcwIaguwAhGCEHRM2NLp1G4qOI4hxDdEn8LEKUBVRF47rRzbHEmshXraT2XhgW1UvpvRBd06ssbMpLkRMzzugtq7sL+GlRF7LXjcDsL8YuaQx6eAfTX/iOUetfJyE5HZdXjRh9/PpYNnkFhb8q8itJIjk+nfWfu5gzdiDPLZnM03NGU9OZ8Jse1M4UeS7yeknvdD76X2BJSJIEAt4yes3ZFKbSPXj5brDE/49v//2haTq2OAt+NPy6FsqYUAWD6+9qzsWd6/H5zu95fekXBHwaZXnlIWueGKcVrVw/Y0aFySyS583ju9yjCCYfM0e24f3H7mHjiAFo3ypcmGIjV48lpe+SsH6S1mcRhXsWkbWwI3lrh9Fl+GQAVozsyEM9W7J0yM1oqoKl+Dimf48ib0kw+miV9Ij9SIqtuo9yYgYOZwrCmys4uaQbpa88SlqfBWHHj+14DzvnjWNGpxbUz0xh7e63OJHnQteNMO/xB25txJr3j9MsI5a7r6uJrPmZdVMdWjdw4vIEWPP+MWremI5N87OmUwNe6NeCjT2b8cpXu5Gv1rh8Un2uvKsRAUcgmjL8J6CyfvzzY4cYvPE+uq0cy7KXt5BppFO4w8fHm7/lqn4XhmWMXNnnAg69egxPkQ9/IMCOA/+h5bDGYdu0HNaY5/e/ytqp/cMUzhtkOiMStxQrHPypMNTmKiEnZoTmhLjO0ylxe1g0uD0xagnoARzJwah+neZX0n/6wwhvruDEos5Yd08hXnOHfZ5cs07EvmB4Cug0eCJXdxtCQ5uX5N1j8T/anbw1g7B5T/7pjhp/F5QFyhBlE8WqGezOM9470Wo7w1yeT/bS2yncMg6HpPPBznV4inJ54f0vqivjTx1I3sfBcpmow0gU5xRCNeRVEPRAiJAjWEF3g1BFyIkqrUfxD8BfgA6dQzDgyAfZ1PxXLco8fpJx88Tie7mhxzAS02tjscXg93mw2YUgCXckIVhjSeuzEN3niuhVqpUWYandNJjGKMlYrHac1jgW970WR3I67UbcT0rtBuy+vx/Tt/6XV/YeomZqIqsn9UUpKyIxNobhq17k5dUPcGxfUJTo5yK/iigyYdcBskr8vPl9ERAkHpt6NUf6DcvzkhQ58lzgg4deP1Sx3zN997epnv9q+EtYPaIdjuR0ukxZij3eieL14HBmoPyDo0WapmMyy3z0+L5QNFC2SHz57BGu7HMBWYcKuXHqZWiqzoZ7BjJ4XlVGxYZ7BhFvtqIqOnHE8fShVxh8RTfM8WYSTVaOfpzFpX0bIftdNDApKJIDX5fFmFBJiHfgemU55YfeBsLTzFeM7AjAjQMnE2+RMCQ7cQOX43pnG+5P92CoasR+pPu8wfNPzCC193wKX1wWIkqVx8kYuhpPcQGSxU5JaSlXdOzHa6sfoOv0R7m9ZSMG3nAhdsPL2jubohkCZknkhQPZYBiMvCIpTMxr7dSBfJRdRo/GDm6fXSXWtG7qADa/vZ1vc3/kgppNcAk6sixiNgJU11CO4pfilzoynF4/fmntJmzs9RB44JLuDTj06jG+3H2Ey+5sSGyyjdKccr7a8wOFR0uJcVqRRIkeF7ejsPgYbcbUQDJZyXL7McfK9L6oEzmefJ58YDhFHg2XV+OnMg17hOyLk0VuZm5/izWDHoDd94fVIWNzkNR/eTCKeuIHrDFxLO9/Lc7MOvSas4nnl91Dx5HTce+ZF57u/sQUuo59gJQBUscAACAASURBVKVDbqbtoKnkHj1MyhnmlPxnl3D90NXkrBsRto+8rZNIHbYBN7Y/7mb9TaHqKqIBsXY7Jz06NfsuJjeCgFtyu2FnnMsBJEcSJsNP72mLyT1+hBua1GTGlpd4aHg3zstIxmYx4z/hp87tDdG7Gojy/3YkiSKKvwUMHcHwQYQIuSGaAQNDsCKgIKAGRd0AIepFHsU/AFFCfhYhSgL1bqmLN6CiGwIlHg/739hNaUE2/ac/TOHO+0Kq6mkDl2O4i8jbMTsoANN+HOYrHkD5+H4Mb27Qh/miqbje3YPz5lFh4jGpvRfQ8Ko2XHpLL56cOTiUvj5j6bPMHtyFn3IKiddK8JZnU27EAND93hXsfGgMZQU5VZHfCGRT1Y2IAlSqbpyROP8sLPEMXr47TL282/ydzP/C/bP7/TNVzyuj+EVZx9gwvmvo/Rn/+REs9rN6rL8bDM0IS81VPCq+UoUfP8nlgi71+SDHR3nAID0mnl3zxuJVAtjMJvIVCY8q8MWmr/GVKowY2pODzx3FVxogqWcCF/e/EMn1I7mP3x1q13GdH2D4xndYNrgtRgVJrkRlPS0EyfjNtw+kYM9iHJd2QIpNJPHGgciJmRS9voWkNrNRPpkZ6kfmy2eTn1tG5sQdnPz+AIY1LkTCK1F+6G2ct06g1KuweVSVLVrP2RtAU/nPvV1C7/Vf+iyemBocK1e5sWEKTVNtTFy8PqyGftiCTWydOYK+s1eHK8sv2MzC8R0wWVN44OUTZJUcJzPewsNdLyTNHFVR/i2QJBHDo4UECCsjilIEGzkrNjYOmsugjdNJi03ikQ4z+GDl16HvXdnnAr7a8wNvr9pH+5lX8PnO70OfXdXvQr55+RhNrhYQdk6l2JFEYpsh1EqujbuoiM925eArVWkxPp1BT38OQPOMWJbesRDPU1Oqxu8eM3GLAo1qp1EgOak/eBWioaEWnaBg93y0skLiOk9nx9zxlBXkMGjpM9RpfiXH9u1l+30D6XHfKhwJibgjRN4z6jTAmVkHe7yTZ5ZMZfCcZZi+W141pzSdRO6zK1Bd2QiGETnt2vjjrdD+jpBFGREDnxLghjGLObhuEhlDVoGhEyj8iaJXVqGVFUJsOubLZqF8OqtqDLpsFoVvPIWldtNq83nDPotYM7U/GgLJUgA14CGhjplDb/zEN6+d+Nn2HEUUfysYwWcJQwhffg6KulXSkSBZ9331Eua0msHPtWiEPIpzH1Ef8rOElBQHHkWjTAl6Hg/d/hVrb8tk45Br6T1jBab31oY9/GQOW0PejllhXrTJHScjmkCOTQBBwkAkUFZK7uYJ1SIsiX0eZungf1WLPA9e/w6FJWXYvXlsv29giET0mrMJZ82GIAqYYxLwecNX2yuj0boeAAQQghZsBW4FUQ+QGh+LYI77TT7kkiRgKKXklbgp8MGaL9zsz3ZXRd4jeOFKaikrBlxT7ffdteUDvOaEasrAv8ev8UzHOlOd/en4O/uQV+JM1y+ST/nFnesh2E3oCRZkQUDRDb4rUPBrOs3SrOzP9YX+XpViwZfnQfVrWGJNqD4NMdaMaCnHs2FwtXZd1mEeNZMcWPyuMB9fOTGDtJEbECWQBR1DC7bTvN1LKT/4dii66HptHfI1/UhMTwNDQ9UFFE3CV1qM2RpD1vf7OO/C5uStH1nt2GlDVnPix8MYho4gipSXFPHxc1u4bfw8HurUOLStM7MOwze8g9vrI8EqIUoSii6yZNf7PPnGp6Ht3ll1D9eNmlftmr716BTGPHc0bOHr5/rCr7lfvwV/xfb6a37f6W0Ugmm+rSdcjF+vHlWUJAEfXuyGjbeX7gvTSFD9GpJJZO/Wb2g9sTlQoXFhCJS7fMhGGZ7ddyE5kqoRq8Su8/lwt4fzb6vH2DcPh+7vU3eeTy3/UUSzDa28lOK3twRJ94DHOJpbRKIvB+HNFdXao3DT3Szp35pmrTvRadJiSvJOUl5SRHqjFtjNMoUbg23YUrtpUNE7zolhTcCniOi6jtVsIMoyJllC87hQXbkUvrY51J8yhq0le+2w6toNwzfiNexnjfz9Fdsq/Hof8uISD6og4Vf8dLt3BW/PH0TO2uFIjiQSru+PZI9DV7yYkmpR+sluEq7uhCjLIEioih/JZEYLQPba4dWuecLwzZi9heSdskCZ2mcx72wvofCo+4ztOeqh/NsQ9SH/85GS4qAg5yi23AGocb1QTU1Dn1kOrgBLHIGMaxG1w8iBt8h/3gOqRGoLO0qDsQRS2vxh5/VXvF4Q9SH/pyEaIT+LMAyQBYEABpPb1EOKtTNg6bPYRZXS0yIRCIRNygnX98e9/zUSrumI7j6BIFsxVB+yPQ3HFZ1wvbI6tK3qysZssUSszbYIOjUTY1h598AwIbXt9w1kzKa38GpmzLKIJOmhdHCz2U5ZXnZYNLr33C2YbXZ2TuzxuyPUmmYgmeMR4h089PqhKmGrro2DpDridyLXnueVuLlz1+HQ91PNQRmEAreCX5J+m4VPhCj+z2UR/JNQqcJe6UXuK1WwOc3oMVb2niynPGBgNwm0yLBxKM+PWRQoDxihvwG/yutLvyDGaeXGcRdz8OVjXDKgCXppScToXMO0OLI3jK5K4e09DwEFKTEzqM6qeNGVklD/SL51KLllQZuo3K2TSRu4glUT76Tvoh1ouoGk+SkrzAVBILaizH33ipm06zmd0gqrs8p63VXjulFWkEOvOZvYs3QqZQU59Jy9AS0QoE7zK7n1rgeJT6uJJJuQJYEXVkxh/5vPhRa8Jra/CIAn3/iUmqmJmGQpoliT1Ww5u1ko/3ScwQ3gTBU2mmZgwoqIiDXOHOYpXqN5Epd0a8CNYy9GQEBTNN5aGSTtTdvXxZlhosSVTVKHidVUtl27pnHd4PUUFwks7dqYCbuCY12spJKzaXy18/D5vGTEWxEkR8Rod2aNukzY9iGiLLNqaNvQ2DRy9ctYE5zUHLUKtSQf0WJF/fJB1IporKnJFJAt8NmDaN5cdFsa5iseQHJmktR2AMV7nyeh9RD8yKT2XRzmmx3XdzknS7xkxiqIsolywx4VeauArkGZrmGRTay/ZzDoKqorO6SwXola975EYqsuEChFdxejHnsZU72uBL7bhemC/tjqX07ZJ3tC26uubGyGn+wKMl75Xt62SVx0y3LeWOOOqqxHcW7ACJaOIZjC3z9V1M0I/pUSEghkBedOIfDXJMxRRHE2ERV1O4sQBDAwcPtUFr1+hG/yyim01sCckFZNAEZXvGHvSfY4Eq7pjqCUENi3DP/7EwjsW4bgLybu6q7UGLmKWmPXkTloMfYm14MoR7RAK/KDrusRyaymqvxn/ijKs49RnneYFQOuYc4t53Py28+qKaE/Mb0/7qL8ar7c+Et+07XRNJ1Us8imXs15cfhlbOrVnNSfSdGtrD0//fcVVDx3Z5X4mbDrEJoskafo9H38C25d8+lvsvDRNAN7an3GbH6fGf/5kTGb3/9DUuP/jtA0HSFGovWEi+kwO2iVJ8ZaQmQcoDxg8EW2lwtSzCh6kKBX/vUVB4lnJZmvd2MtPKqOLpoiiiIFCo+HHkolRxKSHED/ehGUHQZNQTTHIJhiMAJlqEefR9R9JN86BqhQsC4pIi4pHU9RHmsGX8fWe/uiaQG2TuvN3NsuYMecUVx6Sy9efnIdRusx1Ji0C6P1GDY9OD7kS779voG0HTSVoqxjPDlzMLLFSscJC/j3rKHM69SYVUPbUnDiMP8aci91ml8Z+o4r+ygTOl0RrCGf0h+fJgb92E8Ra1o/bSAWsyWiDZosRsW0fhNEQgJrlfhFNnIiNG1fN0TGk+rG0eiGWryx/Euen/URrz/8Bb7SAKmNErio4/l8+u/v0AJB54xKd4xTobqy8ZeU8/HWb8iUJTb1bM4Lwy/H6bBHbOsWk4minw7j9ZRFFgjTdcpLi9g0sTtFWceoe9GVDFm8haQkO7h/JPDlfPSvFyEKCoLVCQTFw4SDCzEbZWEq38rH9yOqeXBsDakdR/DJe/9l3ZjbKCguxWg9hti+KzB1uBeL7sf079FkL+pE7prBUZG3U6CIIoXlAY4UeNEVL4rfX+2+2Ztcj2SUoHw0Ff97YwnsfzRIxo/sQq7TjsA3W0i+ZRi1J2whc9BiLLWbIidmoKuBiO3JGhO89lFbxCjOBQh6kJBHSlk3KlLWtZJgvbjtgosRrPFBC3I1SsijOPcRHeLPIgRvAF3TmVgRGdn40U/ExdrxmuNI7r0oTMVZjEkg9fbZofcESwyiSQJRxtRsNGKtdpiajcYwxSL4CzG+W4r66d3BB6rbhiOaLdzzxLtMfeI96jS/EmdmHQYtfYZkh43scqqR2WY3dsYqQa97lxJjM/PyY7NDZNtsi4lI4M22mGrv/R5fbk3TkQIqFk1DCqg/H8WuiFqfqnrebf5O1nzhplmmg6VdG/Ngh0YEDFj93rFQxLGSqCvir2vammagyXEYliQ0+cyp+f9EaJqOX9dCKuwGYJFErqxp49radq6sacMiicSYRI4VK7TIsAX/Jsgc3H0ktB9fWQB7ko1v8hVUaxzJfRafpqy+ENfr60PbJ7UdgLp/YZBY2GsgCOD/YErVg+4FfTEECTk5M7SPMlcBHSYuYsvdQau/toOmhko3gBDJbnJdB5YOuZlSj5elQ24OCR5WbmOPd4b+N1lsKF4PveduYfCyXTiS09l+30A8JQW0HTQ1tJ3ZFoNZhFmDOrF8x3+RBZXUJCe75o7mg9X3smvuaDIT4pE0naVdG4eptVdmi0Tx61GZxXGq6vkvUaVWBQNHqj0UXW98U50QOYfgItJHj39N47a1Q+8HAgYp3WdUW1CFYPvzeYKaC2U55exd8gW+Yj8HCg2sXeeEtfWk3gsp9vp5efUDWJIyyRiyisyR60nruwh74+tJ7bOQ/JwsHM60EBkfNv9RnNmr8b85gMC+ZZiaj8PUYgqBIzuRG/QMnYfhzUWQwxcoDG8ugskRIuctO/Rg9PKnsZpN2JOSSaxZg7S6tTHHxZLWZwFpfRchOZLI2zoJG+W/7wadI1ANA6fdxGvf5pNmFyguKSG557xwF5VOEwh8swVTs9GYr3sMy9XzQbZjbjIU7DUw1esaXGzfOwaOrSGt6zjSBj2CaDJHbE+GKEdV1qM4d1AZIT9dwvQUlfVAXtBdQIqPQ0qti6EZEI2QR/EPwJ+Wsj5y5Egee+yx0OtHHnmEsWPHhl5369aNZ5555s86nT8EumrgVQIhclgv2YbXXUKf+ZtITXAwu9ccGmYmIcsSRc8vRnMXktRhInJKbUwWDf97EzC8uYhpLTE16ovyySxMzUaj7H80PNrxyUzECydzYuUw5MQMRs7fxMnsHF5Z/SBdpi5j4YfFzFv+HBvHBcWpmt3YmV53zyF/64RQamKX4fdQWpDNsX170QJKRCV0Tpv//0xf7lOj1roWQJDNzHmvEASVMdfXZdZLVZ7Os9o3pMATYH9WcNCOpv/+clSqU3uKvFhE6Yzq1KdCxKBJqoXPs72hlPVLMmxIgkHzNCvoBs1TLHy6+RCFR0tD38v/oZjExk78rgBlfpEffQmY2s2hXnowvVuUxKAoUuW5xcajenMREy9ElMz4P7j3tKjfLEzNxyOoZdibXE986yG45TjKSkpCbdke74y42GSPd+LMrIOuaRHbfnlJUBG5WetOeIoL2DFnVJjQ2wsrpgcJeMWilTOzDorXgyjJvPbJQV7Ze5AZAzohCAImZEwVjTG40GOEskVU3fhtZRZRhKBpOlJFFsf/Ulk//XsmWQ55iptj5Iip74IohN4vL1IwvnyKuGvuJK3PQnK3TTmthrwACLoReIp8aJ4Akt1g6Pq3mN5jFilxVnJKfax87ivu7tySuOQMbJqX7PX3VKl195rP7nWL+e6j1xmwZAfNWnfilqETEA8tPq39z8R8yVRM9bpiyFWLp4ItDUMN/x2CLQ2j4qG20oIrf9fDpN0+DREfysdTQynv8kVTcX3+As6bR1H0yqqoyBvBrDPQkEWZto1SKFbK0ApzMdvOJ6nfcrylhZQU5OD3ebFWRMRN9bri/6RK2M3ScjH+LxaF3UN1/0LECyeTt+MRUu94gLynqhT3U3vMhFiVtlNa4Fej40MUf38IkVLWDSNIyIUgHVELi+E8EEwGUkIaunoEyvP+H842iij+XPxphHzv3r1hr7dt2xZGyH/44Yc/61T+OIhgMUm0buCkY7N0GjhlekxfyYk8FyfyXNw26/GKdNZ+1LtxKAVPTCZ362Tq3vMMysf3hSZq0/ldUConcpMDwerE1Gx0MMIRKEP9/knkuCSgotbsiSnQahj733yOTpOXMLxVHeRkO2M2vUVR1jFS0zPJ3zYxrD7NvWcewxY/yQ+HvsTmSKDXnE3VReAya+PMrFNlr1anIaAhaSVgCH+cDVkFNM0AOQ5kECWR4a0c5LuVEBmHIPme9dJ3TG5Tjwm7DgGnpP9GXWJ+Fr9GnfpUGAghMg7BlPXPs71cU9OG7vGDDqIs0OzWugA0uuU8YlJtiJKAXzW4PNNGiS/A9i/z6dE4gW9dGg0SDLwvPkJK9xnk73wwmK4e40SzpSE36Inhd0XsB4JsRfl4Jildl/LtsSI+/vEwd17dgPmv/UggEMDjV2jWuhNXdOqPPd4ZEmpTvB56zdnEl6/tYsCSHWy+u0orYdCyXdhi47lxwCSuuX0Ejw5pUy3C3uO+VSheD6riD/WX2MRUSvwGfW+5hgSHHVmWkFTtDBoJOpKmBxeNtGhT/b3QtFO0KH4FbwmghzQSFI8aIucQFDBs2r4ugiBw/ajmHHjpKF+9UsB1PYeR98QkJEcSyZ2nYkqujbsYPtyVQ+HRoBqw6tfIbJ6EbJFIjTORV1xGt7nbQ8etmZrIgPat6Hnvcgo2jQ63L9s+jcv/NYyPdm1k8909GLHmFRKdsYjJ4W1fd32NYElC92Qh2mIxX/EA6rGXkc7rjiFaQ9Zbgi0Nc4vJBL7eAFRZcCW1HYCglaDsWxZOEr9aQErHBfhzjuNsPw5BFJEE4R+dNVTgLqbYU0SSoya1nTbySgRq1zwf2WxhycDWofFhwds/ony6CPPFk1BOI99nHMNEAf/xA+iaSnLnqSHxv8KXV6KVFZI6fCOaZv2504siir8HIqWsGwEEjJDKulpQobli+JAS0zAKjCghj+Ifgf83UbfTxd0F4e9fp6YYOnEmkeHX1GHis4d4tEv9MEEnCFofSaJEmT2donZzuCAzEUGWQhO3mHghgi21auK2ODFfMg3lwBr03A8qHq6mhg1oqisbR0Iyzsw6GLKVh54PKvy+2qc2T0zvz4glT0asT/MU5vDswgkMevgZPnl+W8iDu7ykiOeX3UO/BdsZu/l9PMW5bBjfNSxC+M72FbQbNetPq7WurEGPTbJHFMRKjAmuuP4vsbgoqiAbQoiMQzAa+N6aA7SecPHPXjsDQmS8EuUBA8MgpHYd47TSevxFXNS/MXtPeik/Wh6KpMsCpMSYGHJNHda/f4zRV6ejqiWUH3obzV1ISveZWJwOAt+sw9xiMohmDD2A6cKhKF8sOIVkTMUw9GBWiREg7eh/6XnxTeSuG14lCjfoEQY8tBo0BUUJ8N+tL3LziBnEp9XC48qn3iXX8N+1c+k7/wlinakUHP+eHXNGheymNDUQMcKeXKsehm5gstm5a+OboBtohkGJT2DFM/9l1uDOJMRY8Xp+e4lHFH88To2ui4LAtcOb8e6a/VjjzFzcuR4fPf51mO3Zl7uP8PUn0HjIevylXsrKdLLe8VLnsgx8pSeAYMp8bLqN9E7nM+bZQ1xRJ5710wYyZH6VT/26aQPY9OJ7zLq9ZcSx2ZGQDFSUCSl+JF0kcOxl5DrtEMwJwTnhxxdBNBGoINRChXhb4WtPApB08xIEdBCM4Pzh+joUAc/duYzUzuMQZDHMLxsqIuieQgp2B1PXSz54itiL2+G11fjHknJFVYixJvHmt/lc2yCZ5FgzkpyB4SkMjQ91ml+JLEuoVieCLTU4LiVeiNygZ3Bh3RKP3HQMgU9nh41hAU9wtBVFkawI4n+CHgCihDyKvz8qI+TGqSnrlWWQooyhaWiuYgzdjiD4EWIS0HNBUoKZdkWKwfdlkGSBejHnBm+IIopK/L8R8t/TkVauXMmKFSt4/vnnadiw4Vk8q98HTdNxixITnw3WkMuSzOMzhmC3mCl2l7Nq1xtc2fg8ajkEJK2MYwGVH/LdXFAjJhTNMF0wCN1fjKnJcJTP5oVHOBQXuutrlC8WYGm1DEvtpiELG03Xmbz1HcoCKnNuTGXZRy5cqomRa17BbjXjS8yobjFVXEBR1jE2TuxGj/tWsWbUrUDwwaLdiPsxdB1FKeXlxx6oFiHsMmUpG8Z1Zszm94NR7NNQaaOmaQqSZD4r0XRN05FNIpnxlmqWURlxFl4cflk0/ffX4FeqU4dgBNPUTyXldpNAWbYnjNy7S1X2F+vVIukXpVsxywKxFhNT2tZHEAwcWgBPRQ2lJSUVw1AwNxkGgoghWhCpKumAipTdLxZgvngSgi0N3ZOF84Y70BU/aX0WoHvLEKyxmEwKSkWqu2BL49YBM3lx80Za3TEKTDbe3rqcKzr1JyG1BisG3RBGvjdO6MaYTW9HTGmXzVbefXIl513cklhnKo7kdBbt/ph3Dxxh1qBO6LqOv1CJegf/RVBZmhEppf3U6LoUIwbJOQKvL/2iWj15mwkt0DHwSyDEO7A7oEEtAVESaDu5BbJWhmCoaGI5a97KIavEz/X1k0lIsLN15ggEQ8Nskin1+Oh6w2V8n+3CedrYbG98PYk1z2fRu8fB0JDMVgL7V2Kq1zUUda0k37pSXbwtud3DYAStAZVv1oHiCpLC+j2Cqeyx6ThvvgsxJgnDl414/u2Y694SvDDoKEf/g4EUUg9P6jCRvK2TSB+5CQ+2f2R7NstmFEXk2vpOsgtLSIuVcavg9yihLLJhC7eh5v+E3LBfMGshrSWm+j1QPp8fRsAFqzMYMa8YwyzXLKXGyFXoalAw8PR5WoxmKERxriCUsl4lairoSvAjQUYrcQXHLkNGEBUEQcAQTKB7MQyDw24IGJDjg2QLJJgiHSSKKP6e+NMIuaqq7Ny5M/RaUZSw15r2y+KZBw8e5Msvv6RGjRpn/Rx/LyRJRNENskr8NMuIJa+4lHvXPENqgoPxd9zEqkl9SAoUkLchGMFLT8zAcftDBLBgvmI2gW8eR7ClglKC8tlDp5GPRVhaLsQozwum9ugB0nvfT/6eR0lsMxRdlMnfGEx9TE7MYOkdC3GrJTx2V3scyekMnLEszOYptuM9bJk7EaiI+NVuEHqwuG38vLD09Z6zN1BamBMSvqqswQ2JvMmnXweB8rzD1WzEzkY03azrYZZCIfszh4XCAnc0/ffXoEKd+nT/5orn8jMi4FW4ItPGx1nBtPX0WIlmaVb8pQrXjW/Bwd1HKDxaimyTKS8OvxuVlmgnXD4ee/coPS+rwayXvmNL11qk9VuMLPsJfL02WH/58SIEqxO56RgEcyzmy2YgmGLQVR/48lG/fxIkSygd19R4GIHP5yM3nYTrw6dJ6TgO5eOpYf0o8Ols/tV3Dh6vRpk3wA39JvDE9P6M2fxOWIbIaxsXBNu7IDDw4Z0hpWtnZh0GPfwMktlKqztHowQC5JR4MZkT+PZEHifyXCTFxyKJ4i/KNojij8evKc2oJOcWpIiLVbpu4EcL9Y/gvlWOfZLNhZdDzhNVFmJTe8ynwB1LnE1GA0p9KgGvh5REB65SD5NXPc21zeszq88iCrZNRnVlB8l4u1EI3qyQoKHU6pFgGdNpKdDKx/djuTpI6E5NYzf8hfjfGxuWqq58fH/od1haPYIpRqLotc0k/msQplqJ+D+cdgrRn01ATCD2so64P90TUpT3KwpCTAKSz/+PI+XJsQkIbi+5BcWMXRjMdHh8xhCeePUDHlr9MmZ/MVLATcHLa8ns9yDKF/MxX3Iv/g8mVVtEtLRcGNypoWN4C9CVEozvlmJuMZ20gcvJ3TSuSk+g+wwK9ywioe2wf3SGQhTnBipV1hFMYFSMIRWEHFFGLy0O/m+YgGDQxRAsCHgpDoCiw/mxAsc8Brm+KCGP4tzCn0bIL7roIvbsCXpvGoZBs2bNeO6550KfN2/e/H/uQ1EUHnjgAZYsWUK/fv3+sHP9rZANAUHTyYy3MKplJnctXk9qgoMpfdozaeVTLB96C9rrs8JsnWJMAUyyBmI88sUTMTQ/gjUZ8yXTwh+yvLlggPLl4qoHp0vvIaX7PRgBA1HzkzFsDQIGus+DIJuIlcz0nrGCPY/NZdOD4+k69gFq1mtM9pFDbJk7MUSwnZl1cGUdpcd9q0g/v3FYpDAsIj6+a2j78pKiM4u8+Uuq2ahFiqZLkgBKKZqqIMhmsCUiaj8f3T7VPu1UQSwxmrr0q3G6x/gvVfPVNB1zQKFVbTsYBgFd4P3jlZ7kMi0GNeGLjQdRvSp2k0h5wCDRKtA4phwrKibFz+p3c+jYLCOkB6CJJsyOePSAG3OFkKGp2WiEuHpQnov/g9nh2SLHXsZ04VCwOgl8NhfDV4QRCEYL1QOLSbhyOHrAV61eU3d9jdVuw2Q3YbYFeOvFDfRf+G/QddLrNUYQJZyZdek7bxue4kJEUUKUZO6ctY74tJqIooS7KI+jOQXcet9GaqYmsviuO1i58TlGdb2RWRufIyXRQeGnxVHv4D8JkiRgoxxRD6CLJryEe2f/ptKMX7hYVbnvf911Pnmbh4bVgrNjGuM7P4JZEnDKbjJSDcoVK2WqSFK8neVDb6FJ7RQMAVIGr0LwFCLZ4tDdOahHVlQROUFGjIk8JxhKKf73JyCmtaz43IMg2xBrtUPOaAmiGXOLKShfLAylrBt6AEFQSG43BEQRZd/j1cTiLK2WknLrcJLbDwfDoPa9L6EjUGboWY3FnAAAIABJREFUWGOsGLpOwKuEjdX/6z78rSEIeH1KqOwAYNlTrzKt760kxMeQs3Yypg4TkRxJIMiYmowMfi1CzTgG+D+cElxsbNgP0RSDpeVidF8RclwCmcPXYugq6BplX71GQusByDYHDqMYQzbhl2Px+aIDSxR/QxheDMECnPK8pgdT1g1BRiursNUVzAgVhBzZjiiVU+zxImAjRjJwyOBSglwimrYexbmCP42Qr1mzhscee4zvv/+exo0bM2LECMxm8//+4ilYvnw5HTt2pGbNmr/pHJKSYn/T934pJEHg2CtHWdWjKUbAx4k8F7MGdWLSyqc4keciJc4aemCz1G5KWvfxqF8twFdBNExXzYNAKf7TU9W/3hAkHP7C8Aenz+ZhabUMpbQALHYMt4u8p2eGra7HHHmL/tMfZsvciSwdcjMzXzmKKSGNsoIcgDDV6GP79jLu8fci1szGOlPDtn9n+woGP/IcjuRUTKbgMmVKiqNi+6KI+8BQQ9vouk729/tZP7ZTKPJ4+8JnsNe8gFrJsb+JYFfu+/8Df8Sx/+j2CmAkGNw05VJ01UCUBayxZoT/4YdtGAalfp19uT6apFqre5IXqzTr3oCYGJmr4q18ne+lMSdxbZxE2SnRQ5fdRFaJnzGtapJqcqHsfxxTva7oEPLuNTe7C39F3ThUZYsERZOCKeuGrwjzJdMIHFob2kZOTEeSDZQvHg3vS0d2gaEhKKVYDLiq80D8Pk9oEcqZWYc+87by3JLJwTryZbuwxyWGZXsMWrabxIQ03nl0GjlFJczf+iKff3ecEZ1bs27aABxmGx+8sI8YpxWTRSIuzv6H3Lv/z/YeCWe7vf6S32foOkruYXK23B0a99L7L8GcVh+hwvrQU+SNGO2WJBFD1FACKmaTTHJ8DGLFdwzd4LoRzXhn9f7QYtV1I5rhSLQRd0r/KC/2cUn3BkhyZB/p85xWYk35aB/NQPXmYrGlEXP5A6QIMrlPzSK3UlG79wIKX1xG0i1jkKw21FM1RQQB//sTIs8JSnHQIvO8jvg/mBIe5f7m8SrdkctnBom9bEMPuAl8sTBiORQE+w+6iv+DyaE+I9XphqGasZs9/B97bx4nRXWv/79Prd09PUvPMCvDIigIihEVVMQYNd+riTFGozEuaBDFRMSAiqhRVqNsihsaVGKQRJO4J3p/MTdichVQcEkEVwTZBmZh9pnurvX8/qiZmmlmzNV7hSDp5/WaV0+dququ7jpVp57z+XyeZ22yCMuD4ypjJEwlSCv9J+fh857LfY0v0l93t9nUtVoZmjBvf7yNeSte5Mnp38dt3EXyw1cpOfNKrNXXBkT86Ft61b3wraZg/bAJmSUIo2aBbyPdVmp/fzteaz2l4+9BJpvZ+VA3XYxxC/HzKmlNOdQ2ttKnqKvf7m/YH897J/bnY+sNX6S/7q/fLWq6uFaM/PwInTObvi9wADNmkkq3IUwTRTVBsYnHTZxoHoJ6nMYtFJYeTn6+iaW5NNXbGLk5FET/7346++vvBfv3sWXx5WKfEfK5c+eyYcMGTjzxRP785z/T3NzMrbfe+rn3f+edd9iwYQPXX3/9//oY6uvb8P29M2NfXJyLJyW1HzUx8NsD+bjZorIkQUE8Fg7idS1pyjpqxIq++SPcf2QSDZI1PRRv7XcWBtZOqonUMm/IwYOTA7qGAuzqIOPQodb71FzKJ9yPU7+d86Yt5JfTx+EJFb14IJOXr8Z3Uuz8+N2QjAO0NdT2WjObk1/ILS9uRGgGHgr/ccMScotKsT2PpqZWiotzqasLbHUMLcKVD7yIEc0J039bd1eD0MJtVLclJOMQEPbf3/B9Tr/zL5hqX1Tni1ntdP/8fY09P/vLuoHuzf66Jzq/Q5tl/4/bRuIR3t9tM7jQxPFkD4E3QxEoBYJmO43e6nBUnkv9cw/R59wZ6Pkl4Pt4bQ3EIg4V+SY/GpmDvXpKqExsHj8fe/ta9KEXI9O7exWdkvF+mMfPB6FinnAXPgJj5A0EIUyJVAzsVXvWnAdlH/aGpWgDz0QDCgrLESKHG37zMlYyScOubaxcfhcX//wxqje9x0sPzuHs6YuZ/OhfaUtZ6EaEHa0eXn0zScsmkRvYTVWWJCjIy2X2f23jju8WhdkGKdelre5//k2/KL7M/r4/9tfP+/3iaoraDhIIwX2vevl1lE1chiXjuEKiSdEj2l1xRBFbGuqZ0E1o7ZEbx1NR0KV1ocaU0EpN6IJ2oKop1ZWVowrSvsAqMLA9r9f637yYgrP61kwV83UzEIMnZxxz7W+mU3L+HPT8AoSmoJxwD8K3EbGSkGh37h+OCWY+Ts06jCEX9Nxm7czg+jjsChAKfroJFPDTDSjRYszj5yNTdTg7XkZ6DsbIG/DbtuNufAKZbgChZlwzfttOQEE27mB4YZRV9VFe35FkTN8ITsoiLtrwrSRF37mWpr8tx9q2gerl11EycRnRwpL9rq/CF+uvjqHTmPKoLEmE4/lRh/Rn+rknIFHQEuUUnHge9rqZiEghxsgbwhT1HinrJ9yFcdSNPc9Zh82pu34JZefdjK8WgKKh5sSovOqB4Dgaaml8+WFyT59K1dYayhM5tLn5SCMP296/oub/yjH5f8K+OrZ/RX/dX3/34uJc0qlWVEyamlJ0EnKlpRUTsCyJ3dCAGs/DdVVULU17u42q5KD7IHd/QqRsOE1NyTBLaUd9O475f4uQ76+/F+z7Y8uS/38t9tm06quvvsqyZcu44YYbePjhh3nllVe+0P7r1q1j06ZNnHrqqZxyyilUV1czYcIEXnvttb10xF8crpCceOUIPAEPv1HNnVPGkbRsKksSADz1xkZKLlqAlihHjef3IBpCi/RKPpScMpwPHkbRoiiJYV3bR0vx26ow4rmgmb1GaLzW3ex+bj59iooYd/fzTLjrab57433sTEoUPcKzC6aGZBxg7fPLmXD3s4EPOXREBJ8lkl/EJ5TxanMBq5vzeKs1l9erUqFVRSdUVdDasIsnb7uK+8Z/g2cXTOXMKXfw41/8Ccz8cDvPs3uNoueoHu4+IqFZ/C8hBAMKDN7ZlcLyAoG3ThSYCoWyme/deC9jfnw7Z//sfjzPpeCU8ShCsGvZ1Wy/6zxqn5yFkaznkR8OR8FBdCgTi0ghUjEwDjoDe91spN2EiJZmfLwy6AcIuwlrzXTSL4/DXr8E4bRgrbmR9MpLsd97CCG93om83RpEDbUIQougawL/3QXw9i3kRh0q5BounXEHZiyHeGEJZ147H1XV2dWURs8v5dMmh8l3P865tzzAzUufJmXZ3DL+uyy94UfM/q9t1CcdVEVw8tQjEVlBt70Oxe89Mp1qaOeVxX9HtntIFcZeeXiQck6Qej7k7P4hGYfA/eLyeY+S9rpU8T3Px/I9XFVSlXYZ//i7nLH0TcY//i7NPtSkPMb/9l2+/8Q/mPFqHcUXL0LrECbUEuX0uXgRfocLQHfIVA1KJJrRpuYWYRTk4H68DGk1IDQjKE9KN3z2mPDRrzEqv4G0mj+jr7eQXjkea/UNCOmBkYsQEmvVVNIrf4T990Xog76Hu+WPpFeOx1m/BH3YFejH3o7fUd8pUzXIdAPOu3ejxyOo8UJMgsnSpCNRBERTVexaegU7H7yc+hfuovC0qzD7H47buOuA8TA3VIUXPmzkzinjqCxJcNQh/bnjh6P54Ne3UbtzB4nzf46Wkx9GvqXdCohezwvSR3YT4+u+Tui5gdbF32/HTzYg7Brs1VOxXrkU+/Ub0HNU+pw+AUNXKXt5Fs7DF9O4bCJG246gBCyLLPZjCD8JIkrg1dLZ1qmyruK1NqPEcwlihRYIBSIFAETbtmJ2MBZdBOSlNWtiksUBhH1GyJPJJCUlQdpzeXk5bW1tX2j/iRMn8tprr7Fy5UpWrlxJWVkZy5YtY+zYsXvjcP9X8DwfI66hqgr1SYcH19ZTVFTMIzf+iMqSBOceewhNKx+hfPw9KPE+PYiGdNM92gLSvT2IWiAxjvoZkVOXEznlV5gn3IW782+AhxZRyT9pXMa+WqIcL9kSRmCSnsrbG7exo7aRxb/7E0JRe5Dv06+aRW75ECb/ahUz/rSFSx9ZxSfmwSjxIqrbu8hFgSkYaraQrtuBJttI2i6ReATslh7144/fMh49mptRT6iqRvi5nSisGEC7pwYe4lnsv5ASUw1U1j/ebTOyPBqS8sqow5XzM4mObdv47U3UPjk7IypYs+J6yox2/LaqUJlYG/5jBBIptKD2MlqCecJijJMewhg9J1CEHvw9nA8fQx8xKVh32BXYa2eGD7ja4O+DUHq9lqTVELy6aYSZwLeaAp/zjmigNuB03LdmEzM9nr/zOn5x5Wm0Ndbx8oYtuJ7Ptff9NuO7XXvfbykvykfVTeqTDnPOGIqmguV7WTK+l6GqAqGqIQnuhJYoJ90uw1px4YHosDb7zuzjOHnqkbjS72FJWVKQS2HEIRFpIz+aRusgOLaihCKSENgs7mhKM6Vb28qNjXwsS3Au/AV9b/4jfSctpUbk4Au993t6OpXR1uf0iTgfBSUbQgjstbMCMuy0fuaY4NesDsh4L5NWIlqKtAOBpM7IrCK90LkjbF87E23A6RnbCacZRVExj1+IUjom1Gaw181CL0gQMQ2OTaQYnVOPmtxN7Yrre2RmFZx0KVqiHF/8y4xcvlQU5uj8eOwAnny/lduuGc/Sn57DUzdfwOizLuXR639Aa1sjCAVtyCVBGrrd9JnjOUKFSM/xX0RLwXdREsPQR0zCKCztkUVnvzMf4bei6VA+bg4Vly1CzS2idsX1REnus98jiyz+V5AppIjQnZB3irr5aPhtrSg5OUgZEHKBAC0o+XLe/QN/mDyW5p2bEQKiKrQdGPN9WWQB7ENC7nker7/+OmvWrGHNmjW4rpuxvGbNmn11KHsVvisRUjLr20PoEzdIo4IW4fYrv89h/UtIvv83ap+ag5uyMEbNCgdlES2FeD+M0UGbSByKMeZ2jK/fhVIwAPPEhUhFIoWKvWFpEOFYvwRjyIWAwG/ZQsHYs0JSHjvsJPr+eAlmn5Jw0O4kTUcd0p8p3xzGveOO4/e3/YTzbnmAW174mMnLVxErORjb8fG0PCLFlXxk5dFkSSSE+xeYgoHpTSy//ATmnD6Qe8cdT80nG9hQZ+F7vfs2++4eU5lmPhPueT5jMuAHC56mpLQMw88Smf0ZTsrG1AQxXdCY9ni/1mJEaYSvD4gRUWQPomNZNooR7TWSKfBwP34MJacCd+ffEFoEe/2SwJ5J6OCm8Fu3gu/g7t6ANuB0fDSMIyYi4n0glo/UDfSvXYVIHBrU3Oox/NTuwGKo2/VljJyGu/VPGCOnQ7QE37NRIgUQCxwbukeoFLeFsybfGggSTjmbC088HERA2rpjR20jnueTY2rc/B8HUxjTs/13H0BVBdFUFfV/WEjxubdmRKYT58zjHy/tBrps/Dqj3RYelu+hqUqYuQRw1JD+PPrTU5Brfoq98iK81deQK6rRVIHb4ZzRHVFD7dFmagoJvRX5xrXYKy+m/L1pKF4SbdTsjH6ofW06GHkZx6wX9++aSIoUo4+YhJIYhrvxCYyR03r2442B1ziehbv1T+jHzMAYczvmSYsxxtyOfuztuNWrMY6bjXnSYvSvXYVUjM+MynZfRjHAd4MI+qGX4O5a3bXO95Bt9eTau/ndTRfQWruj1+tajScoGbeIFHtHP2FfQxGCsqjKdacOpqggjuwY5zrdRsxIBImPyO2PfvQ0lMRBKIlKjBMXoZSNAQiFWP1UHaSq0Y+9fY/zOh2px9EOvxpn/ZLQGq07ZKomyKSz6nHfvA62LqX07MmouUUHTDZCFgcuhEyBYmY2dkTIfdsB30OJRgEdgQfCQ4pAayquWjRu+5C/LLoSKSURFdpd8GU2ozKLAwP7bPq6qKiIm2++OVwuKCjIWBZC8PLLL3/u91u5cuWXenxfGhTwbZ8n3qzihv93MBN+8w+KYjqTj+vDxl0NFCbKsbZtgHQDzpY/ZCiwKoqKveMV9K/fgyIcpNuKwMHa9RB+62sIvQxz4Fz0EZNwjDz0ft/EWj0tQzAmcdIF5B13LiqtoeWTiJZSfuEskm4uRw3pz9TvHsszt/yQhp1bGXLcqfQfMgTD1EFVSPldfqduyuK4vlGa62tI1jYwKs/ko3QuA/R2mqp2ctHPl4c14o9N/R6XPrKKpNR6rUFXDRM1omKlA11jz5PESgYzeflqPM9GqJ9PZT2LLw/dveJbdrejqvHPpYrseT7ClxxVHuXtXSka0x7ra9KM7htFFUpGnSWAaRr4TamwxtbsfzhF3/wRal6Qnq4PvwKQYS2sfsytCLsZ+927EX1Gog8dh0AE0UPVB9L41k6cmseQTgNmv2k47S9hHH0NqHnI1m24O/4bbcCZQa2tkYvQ40Eq/IhJSBRI1SLw8dP1ECtFSQwLldo7o4t5hX2Azgklm1m/+jNzrzgHIWDn7iYeeGYltU2taKqCaphUapL8iIqTyubR7W1ESYaRWa+tnqLvXIsaT0C0hP9evoP6LW0UDYwz8owSTKUJXWgZqt8RVeeRG8eHqtm3/ehkYu9lWk06624lZ8y9WG6cinwzg4CnbK9HW0XUwlg7O+M9vDem4x97D8pRP0dz2vBaGmh89Q/kjTmHfpMfAunjeAroBsKPB/ZkkQT6sEswRt+E9CXOtr9ijlkY1IK3bsf5YFmgmJ44FHJL0Y+cBF4L1ta7kU41Qi/DOGge+uDTsHbcEbaZuXNRBv8Af9Pvw2PujMoao+cE45CbBrMIiYc+YhJ4NsaQC7HbtgZZWoqGEoljei4TF/yaxrpdvdbOq/lltMnPdz/5qsCxPfIjGkZeBJGMZLiNtDTsJh5JovUZBJEYvr0doUSQfhrtiCsQh18FqVqk0HHfuz8QojwmsK0LILHfWQBGAuPwKzGOuhFhFgT3om6kvDO7B9mhTN3hKtHn9MkZ2QiaKsgxUijSxRca7XYU9wA6F1l8ReGnQCvJaOr0IfeTQYaHEo0iQ79yl+bmNmJAfl6MEWdczt+fe4CaD94geshxSCDtC2L/d123LLL4l2OfEfL9lkB/yXCFJK7Aj08YQEvKYWezxc5mi/teh8XfG0zOuEXUrbgeRddxa1Zj16wO9zX+4ym0wacj/EbSW2Z2PUj1vwnba8BPvo+15VaM4inoB52F89GKnoIxYxfjt1Thfby4p2DM4MksvezrpLQYDTu3ctw5l3HuNdNw3woUgEW0lNgxs1DNSlJWQIyTW9/j0Z92KaFPuPd5VNfkyduuyvApf+G+n6H5Nhu9Pky453mWddvnksXP8WE6lyFxDTNCBilHzQW1I4HJdrOezfsI/1eveB/Y1mQzpl8M25NYnuTD3RbDi3J46LpLmXjn8lAsyzAMyCmg5LyZNK16gj7fvBB3wyL8SCHqsCtwNj2FduiPUPQ45pg7QVGwXpuK6DMSbfB5gdhh7evoA79BetOsruui3zTs6mVY2xdi9J2EtXUWRt9J2HVLMIfORtouqEYwA68YSMUEkgjhI+J9kB6Q2g1uOkiVl06g7N6hLN0ijgeCCSXLF5x38iiuXLicWZedxaxfPs9dk39ISUEevmpyxePrWXzOcPjy9duy6AXda8etbRuoWTENgMppz5NucSkaGGfMOXEanpocKvuXjFsUejl7nqSiIJ9nfj4J1/Mpz7FxNvZSqy1dDN9n8TnDw7T1inyTyoJIj7ao6oXq6N3fw1A9PCKohoF0XRKnXITitWKt6lDjHnYF0lEReBgn3AmiDWvLDOTOjn4+cA6+iEGqGox8tGHjwEwgNLA+vT7o81VLkE7gnCGdanCqsXbcndFmbbmVyMF3Yrft6FJfHz0biYKztstOUz9uHsJLhlHaILJ7C1KL4bS1UfOrqaHid/7ZM+AHt9H8+1vCtpJxiw44Mt4JK+0Sj+qQKGHCvc/zpyUzGX/XU7zz0u8ZOOmn+E49Qnrhbx+cv9mgRZFS4m64P1SzDyYJNZy3bkMfPhEI3CU6xd6U0jEYo2eH5ThhFF01cTfcHx6TTNVgFPen2Y0BEk0V5IpqnNVd43ruqLm0qmVZUp7FvxRCpjtsz7rBt5GA39YOgGJGoEOnQig2H/x9PeVHgq5JDjpyDOtffISNf32KUUOPA6DdlcSy+glZHADYP70yvsLwPB9XgX45OvlRnYr8jpuPEFiOzxY7h+pTZ6Hml2fUkCmHjkdR0uA1YXWQceh4kNp2B3rZZeGy0KPY62aFtX+d6Ewp1EsOQkQKe6xTIlHs52ZQkcilsGIAZ151E+5bmREd981ZGKIVVVU6/MQzldCXXXMW9VWbe/iUn/7jGbiKge2DHhnIpGWruOHFT7n0kVVsiQymut3nzZ0W6NmpzP0Cn+EVj9X8+faXkr75Oqu3J1lfE0QJDy0yUVSFwQPLWTF7EquW/owVsyeBotL+7svoRSWUnnMdQnEDD95DLsCpfh1t+OXgu/itW/GdDqX+Y27FGDoOnDakqmAcfCbW1lmZ18X2heglFwTXhJqb8WptmQlKKyIWg3gx6CBUF+kn8a3NWDuXgFsNSmDZJ3LKkHoe2oDTcTY9g1NxFs/fN5fCigGMv/tZ7v3D66FjQufrtff9lnYPNFXl0QuPoMRQstkd+wi+ovdaOy5VnZOnHsk3Lh9Iw1PTM5XM96iz9TyJjkZUNZBC671WW2h4nk+JofDohUfw4pXH8OiFR5Cv0KONz6gXR/rIN67DeuUS/A8WohpGqL6tHXIBTvPmoFTj7fnItk0BGc8g0jNQlDQipwglJxen9QWQteE40dnnMz5XifRok0410mrAOGxihwbJYtBzcdZmqsCTrMZeN2cPi83b8Foa8ZqqM37T5mfnYOYmKJv4MJU3/IGSicvCSY8DFZ7l07SxiU2vwNk3LyGvuJxvXTkF6TSgaAms7fP3OH8zkdZmRE4M7bAJiMShQd9q34nwnTAzRzvkgtAGDcCvWY2zfSXm2MVEvvnr4HzFK3E//k1I6qGjj6lG+JvnGCmcdbf2zPYwUmSRxb8UMhWmoIfwHFB0/GSgKyWisY4achDC5qP17+P7EqEJosKhZOjRbHz1efQOqfVUNoqTxQGCA0NxZT+D5/kkPZ9cVWPxOcP5xWtbmfT1AbiuRXNrisL8XBxHYIyeE6YoagNPB2mh6MW9PkgpepDmI/Qy8JyAYOcOREkM65pxj5YCPjJZjT7sCpwPHs5Yp+YWU/mT+0FVmP7EGlTVxUnVoCSGoR1yQZg6L4XAiOqkkr0roRvRnB5tffofwiaZz8gClXd++R4jxw1jbWvnnbLLoxqyM5n7Az5L5d73nM91V3BSNjk5EUxVYXiJyaYGi8GFJmu3Jzm6PMqGJpVERGV4iclu26LihG9nlFAYo2chjQRG7gD8dBNCSJy2KvS8QSA9MPsgFQURM5FuA9IRvV4XQitC6GVIrzV8DdcpEaytM4PoedUSzH7TQTFx6l/CKD4bu+5p9NzvgCcQZgEyXY8f6Ut9zv8jVd3GWdfdSbSonOqkwqvrN3HqqMOpLEnQ1BaQuh21jRREVGJ4OI6fze7Yh0gRo2TcojBtvTMym5QxPN/DFL2rrysKxEmh+A6+oodp7O12lNxRc0MiI6Kl6KPm0moHisCe56N6PiqAR3iuu7e1+xFyR88NCa6IlqKOmoP33oMZ5EhazUFkfMQkKBiGkTsIu/EjzLGLQfGQ1b0Qaace6TV19ONpSCWni/R19v1u14f00z3ahF6GiJSA6yEVA5x6hJGD/rWrcD56Atn4YbDdZ7l96CaKIbtKTuL5eG3NqJoOvktKS3RYbx1YZFxVFVItFiZqEMLwQTNV0i0Otp5DwvDRtFRwH9Pyer9PKRGsLTMxKqdgfO0KpEiAm8T3PYyR03E+eBj9sJ9k/O5KYhh62Wis17p50I+eg3bk9ShuO0gPe8t/opcdh/RSaGoM15Mo0s3wse8c24XwAk2EA3iyJIv9F1L6CJmGPSLkwrdBMfDaWkDTwNCAYKLc81Ls+ORjUk4FqumgpxvoO2Isuzasoe6jtRj9jyOZHXizOECQjZDvJRi6ivQkhRGN608dhOa7RA2NyqhNid9AzdLLcZIW+vELMI6bjfAaSW++HlAC0t0NQi8Loi96GWbf6Uhf7SDfAu3IaRhj7sQce2/gsfzpiyiRAux35qMNuSTYP1qKMWoWzvu/wPrreOzVU1GtahRFQykdgz5sAs76JVirpuKsX4LitlDb0MTHO+t7VUK3U+092jQzh35ph3d++R71W1pwU26GHRZ0isJlHwb2B3yWyr2i6p9zf4Ei4Ji+EaKaYGhRYIOWdCS2L0PBtx1NFn31Zuy1M4KHysSh6F+7CoSNokp8JYKSWwxmFL3yeGS6Hnv9/ZDcgWz7BGvLjCDa5zX1el0ILYE5eBFu63tBLXntE+E66bVmRs23zwevBb3otCC6XnQaQo8itAj4DvYbN9Gyu5YFF36D+yacwj2XjAUpuf/ZlSy6+nyefGUdi64+nweeCcpvKksSbKqqZUdDU9ZyaB/D8ySpaF9KJi7rNTLbWwQ9NvwkZHsjtQ9NYMfCs6h9aALRVBVqB0lplWWoY+7FOOU3qGPupVV+sRRf15O0+mXoY+5DP+nXiDH30Sbz0QacHrgBjJ4TaBX4DvqwK3Dba4MaX8VEz+2L9dpU/ObNvfZz6TV168cLEWok3M6pfQKz37RwWehloJdhVt6c0Wb2nY7fWov11m1IdzeSRqS7G6I6+sirg5p0PtvtQ4kXIMwcSs+eDFuXhqJiKq20vP405gFovaWqCrLd488L3uKFma/zyuK/IwDX8hh54RAK0jVoohVQcOpfQgr1M85faxcxr5qPcHbjvDkXIV2kloM+fGKgddHtd9cOuQBn0zOhm4Q+YhLOh8tR3Hbsj3+DtWY6er9TcOrX47x+YxgB9zuyPZQisa6vAAAgAElEQVTEsIyx3Vo1NRQqzCKLfQ4/6J+9payj6PjtLaixOEgRRshbdu/A91ySSiGqoaClGigdegxCUdi69iUMJRshz+LAQZaQ7yWovuDtpzdiexJPQkO7RXvTbryGKtJvPEnRd65Fyy8HzUNIN0xTlAjMftMzH6T6TUcKDeOge0BqKPE+mGPnY2/5I4pQwLOxXrsmEMQqG40UauhVa56wGPP4eTgfrcCv6VLLddcvwGmqxTj8qow0ucAKZwZtzdXMf3IV59z2eIYS+qV3P0d+xaCMtgn3PI9KHm899gH1W1rIKYyQE9MYVWGGpDymC46pMMHJ3j33C5j5TLjnuT3O43MZXvGfBcNQ8Q2T17Yl+a9N7azenkQRCqMqIiQiamiFVh5XGJFIgaZgnjgP8+QH0Y+Zhl2/hPSWyaQ3/RRBA1bV/aQ3XYG1eSpCs9GO+AkYOkq8PLgmvFac+pd6kA5z4Fx8L4m0dqAVnY7TvA4/+X5YX+7UPtFr1Lwrvb0A6aSQbhqZqkVES2lp2B1+z8KKAWiGwZzLz6Z/WQk3X/pdHn3xVd7+eBuVJQkWXX0+d//uzz38q7PYN/A8SZsXpcXPpc2LZqRJd0bQO0l5bPhJ9DnzOmr2sOjqnsbuepLmVITGdJzmVORzk3FVVTAVlZiqEjfSCOnSUufQ2KgRF80Zk536sAmgRYNSjbLjAgInJNJpwDjuVsgfgjlwzh73/2kB2evej1GJDFqAedA8AOy6Z4kMWkh06GMYfSchRBT777/AKJpEZMA9GEWTcDb/F0pOAcboWxAq2DvuJr1pCvaOuxFY6IddHhDCaAnG0Tf1VABPN2AkErgbFu2hTzKTwpN+QOPLDx1w1luaFLy2dEOg1k+g2v/20xuJ94mSn2tTu+J6hCqQEozi7+PUvYA5cHbP89ftXiSdakSsFOOoG8FtR5gFIFT8dEPG7060DH3wOZn9Z/A5+G4K45DzQ9s6o+/Xg/IHHPK1dmwZQx81t8uCLZu6nsV+AOkH9wYhMif9hd+Rst7WhpITByRSBtu0Nu0EwM7phxpR0VIN6NEcCvsfyta3XsZUIOWCzCqtZ3EAIJuyvpcgPUnVu/X0PaUfamGExpRHRLZTVlRK9ITzqXtqLpVTfwl2I+iFSKcaNfFtECpSiWJUTglVWqUSBQlCtmDt/HmGqJVUdISRm6GSK0QQ+Zap3VhrpmGesDgk4+HxpWpQFR/Z8f+e6/JjOm9v3MbPfg9Tb/0thXGTspJi3k/nE9EE16xYg2dbQUTVzCevIIeTpx4ZqH0pgbid5rkcX2kQpKlLcNxQ0C2Lfy0ClfuDmfyrVfiegxGJ4CqfT4hJiwZkPChBCEoR3qhKMqZfjOElJu/XWuxosjgmvxHh7CZdNb9bn52O6OjvnfWVRt9JWK2vBcs7bseonEJ6y42YB81D6GU4tU9glE3Arns2IBtqAUIvxK77I179b0PhJK3POejF3wNpIX0Xo990BAq+U4MSG450GpB+oFAs9LLgOEQxivSQTivGmEWsf+geICDj597+BBPueoba5lbuufYS/vOTJD/70VlcceZJNLUlWfDr/+Ttj7cB4Hp+Vh5hP0L3CLqigGxvxG2p7T2NfQ+7KFUVREn2SGvvDZ0R1L+/+CnHnGnivhlocsSipRQcvwh7zYw9hDcXBtHOQd9FehbSbUMYErsuEAEzD5qH0/J3IoMWIN1mpNcU9Pvis7GrlwGEZM/aPi9wGRg4B6kU4Hu7cXbeH7QNmIcwC0HLCfREIsXouSWkP53WqwictX0+kcFBFBbfxnlvaYYDiPPBw4HwmO/2Ol5Iq54+37wQX4EDqnbDJyTjnah6t56jfzAE4dm4jbuQrgeiCdQC9D7fQhLDHLwYIV18uwq7elnoBmFXLwvOn2ogcvsg083IdD3uew8i0w3ox96OcfTPEGYChIK1R32//c5CzOPn0RlLCdYpgfo6Cl5zNUaugxUpJ6rHei89yNqjZfGvgPfZEXLZESHXKio7GgNq0t5SR6SgGDdSipoWqKl6AIoPGcmHf3kc2VaPbxRhS4GZTfzI4iuObIR8L0GogpzCCJ88txlDE7zwYSOFiQSRnDh1T80NPJiFh133NBKBkjsWvc93cWqfDn0apYggjHIULRdF+DgNL2c+RFUtRKggjNyuWfR37wa7EW345ch4vyBF8jNSEP10Cona67rmZBDxe3vjNsYtepIrH3iRRnKwfTi0TwSp5SHNIjwtD8+TCEVk+Px6no+V9rBabaxWK3jNkvH9DlFTJzeiEzM/X6o6gJSEZLwTncvv7EpxWInOiKJ2FFNBCguhBwKDnQ/+eskFXe/VkVKesaxEAHBqHguyQ5wG7Opl6CU/QIkeEhBptxmZerfrfbfMRAgf6aewdz6M8JqwNk8n9dE47O2LMMquwBgwC9S8INo+cA729jcQTgv2hgex/vsq7NXXc8b4y5n18k6+OfcpbvrtWt7euI0dtY389K7HOHFgLpvr00y59wkun/erkIxXliTQ1OytdH9DZwTd96FmxfV4bY29CsF1t4vq9DfvLa1dVQW5Woo8pZW8iEthNEmB3kRuTjtf+04J4r0ugUwRKUTg90qIkD4CBTwLYZihOJtaMgElMhCj+EykiIJRgogMway8GpQc9JILUHLHYvabhrVzaShoaG2ZgSIc3PoXQoLu7HgVbcQEhCFIb56GtDb9UxE46VQHZRtrZ4DVhEw3YK+dgbVqalBukm4I7LaEglI6JmPfTptAd8MiVO0Ai1QpkFMYyWjKKYzgS4kUGlqinIaXVoBWhMDG2jwd4VZjbZqKXfsEwqjA7P8zzEHzcRpfDs+P7yaR0kFqOkpOLtro2ZgnLghELKUEJNJu7n3yw01Bh6BVIOimY5ywGLdxJ61v/gG/aTsRrxEp1F7PFUIhT7QQV1MHXIlBFvsvOiPkcs84oGeDoiFTSZRYpz6RipQCK9VI/qAjcEUOQgHNDuxUSw4ZCVLSuP6vQDZtPYsDA9mnyL0ET5GMnXg4VouNbLX58dgBNPhRFFXDbdxF7qjvIqUIIh87H8KsuBKn4a/oxecg1DgicjBCKAGp+PAi0p/8FL3w5CCK3oHgocoPxGQ6VNWDNLZZkKxB8W30I66BvMHoo+dkpCBqI26A3HLavTj6qLkZ6/TRc8nJK6WyJAEEhGPZTeOpLIwzpm8EmbayatJfcexJPKqW/CgkHv8ThKBXfQAFybFlDoViK87ma0h9eBH2jrsxyiagxIYDPQl495Ty7stq4tsY/W4Eo4LIwXdj9J+FUCKkP5lC6sOLsLbOwSi/MvN9ccF3whrxPSOAip4Aoug5p+Gsfww9fyDOh4+FbgVBSudMorrL/CdX8fbGbeFx7ahtJM9UeOHDRh6aPj7j2njkxvFEPmftfRb7Hp0WaU1/W07xubeGpDy0QiMWbtvd3xy60tpjIkkstZOapRNofuURNKcWe/UU0ivH4b15LfF4W3gPVhLD0A6/GgS9TnZCUOMuTAUJmIPmET30N2iF3w5muwCBi/QlONWkP5lC+pMfY1ctwSi7GKfxZfzW18LrKMg2acAouZDIoDtxtr2B1ucwhKKAEsUctAAlMqiHCFzGcelloJhETl2ByB+McfQtPVLWMfKw1y9BP/SSkOgF66bhbnyigzweWOOCKyRjrzw8JOU5hRHGXnk4rpA4Ik7JuDtJfbIOITSsLTNR4kch9ARCL0RPnIq1+YbgfrV5OnqfszEPvg+0BG77B1ibb0T4LVhVDyL83Vi7liJiJsRL8YWO0HN67T9Cz8Xe+LtQHNN3UgF5b1pL0annIzfdh/XKJdirpvQ8V6NmU/fMgh6TTVlksdfhd3qLZ6qsC89C+gEVUSLRztagjtxPER98NI4M2nVaUJAk+h2CHo2z6+2/AGSF3bI4IJBNWd9LsB0PM1/j2J8Ox3Zd8nQNB4WdTWm0RDmF/zEOISRW3bPoRafhiwha0WlgV+E0v4FRfBbpHvY3M4kcfDc24DX+Z0BefB/n7dsD7+QPluE3fhCIZ2kRkBJ77Sy0Y37OpuYoxmELGZCv4KPgS4OkY+DZPr5aRs6YewOBIaHRakdJxAg9ejVVIaLqpFrT//xLZ/GVwWcRj/IrH6ZN/eep627KYnTfGGurkpgqjCxxiOs2QrYQ0XzsncvDfqskzgijRAgV300j/Ragsw58Dnb1Y13L/abjpHdhlF4UpJh7Tdj1L2H2/UnP62HbHUG6+5ZbEXoZvrUDxRyIlMneI4CejUg24nz0W/zGD7CbN4VpueF2qRpSTTVMP+8Ezr+9i5BXliQoy4/w3RFl/GVLE4/PmURLyqI57VNWlMCzs2mg+ys6Bd6sbRtoeOkBir5zLWo8gZpf1sMvu7u/eSfcxl0ovsOuFdfhNu6i8JRx2G/eHKT/dqhY41noR98C6SpErAQpwd78IsbIaWEdb0CIZmJveABj5GTsupXohSdjbQ6InN7nLNIdUWwldyxm5dWkt92xxxgwKxgDzIPDiaxQ9E0rxE8lkbVrEP2Pxdo8PSwViQxaGKqud4rAdU5aCb0Mc8Bs/HQSPAs0F3L6Yo69O3A8kBI/VRt6aNstmzDHLkamfoi0m8JxR0RL8Q+wRwrP81FzVP7jhqNxLC8sx/I8H99TaVXK6DPxkWACxalGL/khvrUDvfSSnpOCW27FHDQPp/lttMQ3UPKPAulhVl6NXftcMJG4ZUZQrqYmsOs2YX59EdJqQKabcLe8hD70Unw1gj70UsShlwTZFr6L7ysYA7+FtebGPfRgZmKecDf+4ZMAhdpnFpB8729A1z2/ZOIy2oh+1k+QRRZfCqTXoS/RI2U9jU8w4aVEu7JRXFcQiWlES0fi+gHj1iIC3W3H1+IUH3Ik299+hXIpOyLk2YmlLL7aOLBGz/0IqirYVt/I5fMeZUdtI6cdexizLvsejZ7O8IsXougavpfusF96FrPyEHynBnv3CxhlF+P7dke9bG4galX7BH7yffAdjNKLsM2D0XMPR1rJsLZMHzEJe+2MIIXQTSMiQeqkakT4+d+qeXdXG89OPIZE1MiIcneKGXUheEDV0cK62APZV/bfEZ9FPLzmaqJ69J96Cdu2h4HF1/tH0b1dCHc31ifd68SnYXsNiPjx6PnHYlUtCRTN1QIUvRBf6Ys59EkU4QZiSBWXI71LQY2DkBhGMb61HafmsbD2UrqtvdsJqbkBeamYiPTaQHTYq/Ri+YTQcDY9E/j9dqq+GwVIu6lru2gpTbU1DCo7lMqSBDtqG6ksSXDX5B9iahqD+5joqmDGS1uob7eZf/oQTA+yU1X7L7pbpFnbNlD/wl2UjFvUg4wDSDUg753XRu6o71L0zUsRGvSb/BA+Kgp+kJIeKUQfNgH7nYUQSWB87QqsmkUZ14Gz+S8ZtdgSEeh5yKvQ+5yOtfnGDiL3g/B/JTYco/hspF3Xu5e4L9ELjgffJjJ0OWAg3RqkMFAiPuaom0h/Oi2TDO5cijlwNtaWmfjJ9wMRuMF3BoKGbTtxNqxAG3Bm8CF2M4qiI4WCs/kP6GWjcTtINwREz2tvRlF1nPVLerWJO5DgeT7RQpO2utbMBAAFPnr+U4aedzAR2gNdCqFi1zyG2e/G3icFRQy9YAxCJpHSQ3op8JqCyXjZVbJj16zAKL2Y9ObruvrT4bNwPn0Zre/JIATW2pnd7NBm46s5vaa4e+0NuI6CEisIyXgnetNQyCKLvYIwZX0PUTfPQnqdJRhdE0N22icS04hVDMF33kdKgRZR0K0mLC1OycFHsvPd13B2fURywKH77ntkkcVeQjZlfS8h7TkhGT9qSH/Gn3Eis375HIm8OP9oVAMbMyEC+6XSi/AxUCIDMfv+JKhHkzZ21ZJABbdqSZD2mzsWiRekJxYch1SKIV0HBAOv0HMz0ws3LEUbcgkShXd3tVGRb5JjqCiOnZFyrqmC/GiaRKSN/Gg6a4vyb4DebKG0RDleWyO1K64nTmuvNYaaKsiPp4lHmtBpQ1F1hFFCZPBi1JIJoS2TXnIBeuFpSD+JWTER1NygZtJpQBEWILGqloCzE3vnIwghEUJFOruxtt0eproLvRBr+0LoZvPUCaGXgVaKUXYx6c03kP7kKtKfTEEIDWPAjEyl4wGz8FPNaANODyPiIloaiCfp+YEt1bF34B9+Eysff4Ttu1uYddlZPHXbVcy67CzmrXgR27b59MmNFKd8Zp54EItPHETDn7dlFV73c/xPFmmdUFUFK5Kg8OJAnT131HcpOvV87LU3kn55HNaqaxF2I/bm59CHXYF+6GVh9FsfegFWh3ghdGl8aBXH42x8HOm1IGL5CMNEKRsDQkGghgRcCBOz/42YA+eil10eRFel1aPPK7ljwW/D2jSV9MeXYm2eDn4LbvtmhEwj/VYQItRt6ITf+hoouRh9JxEdugKz7yTsvz8ItoP70W/xq1cjtEj4J60GSFWj9zsZp3ptkAXQAREtxWupx25Ooo+5+39tE/dVhyskI844iPa6VmxLYg6cAyhIpwHfqe393HnNWJumkPrw4qDeXChgDAAlhqKamAfNQ0o/iJZvnZXZn7bOQj/odBQ9ht1BxqErEq7g9p7irkUCNXgkWqIcs//hVFy2iH7XPEzfifciNRNVFcTVVLa2PIu9Btmb7ZmU4Fn4dqBZpES7yofS7S7RHJ1I6UBUuwXHN9GigfUZQMmQowBofnclKQ92tzuksi4+WXyFkSXkewmu57OjNhCgmHbht7j+/t/x0hvvsbOuiUdeeBXbUgI19MQZoOYj3DqsHffjpzcjfAtry6171MAuxOz7E+ydj5DeNJn0pmsR/m7c6kA9XURLEdFSzDELIadvkF5Ysxolp4KULajIN1nwveFsbnLRoiZqhwiVpgpyRTXe6muwV16Et/qarFfpvwH2tIXSEuUUn3srTX9bHkTKm3aGNYaGoZAfT1OU10p+vA1VeEjfA68JP70F6dThW1vRCk5Erbgh6LdaGXhN2NvmkfroEuxt8xAI7F0PdvTdJvTic0I/cGvLDKS1GSE99IqrQyLeKVwlPaenHdTA2Ui3BmvLHg+u225HaAVEDr6b6NDfYB50J9I1QWhBRNxp7YgqzcF2XZy3bw8FEYX0OHPKHSz989tcPu9XnHvLA1w+71fUNrWiqSojzjiIj363kXWL3uaj321kxBkH4Yp/HxLyVcU/s0jrhB41eL0qzVtOBdHLHqHoWxOx183aw+JrFsbAb2G/Mx8RLe4ScTPzes/giBVjfG1CaPVnbb0R/fBx+K4CQkPJHYtRfiXpTVOxdz0EWimKWYE5+B7QintaYPb9Sc+xYctMjMQY7N0vBpNSm6/HKLsi1Ffo3FfgYVctwber8Nuq8KtXY6+bjXbIBWFWVfhnNyHMBPba4PsKoyB4n079ESMfkVNAi53zhW3iDhR4no/IUTELdGY89grSz8FHD8pu6p7radPY67mbgcBGwUGigDEExaxAiQzC6Hd9xjmUTjW4rUEkvTexQN/DGD0rs/b/mFm4yVSgBu/7lF52L6Xn/DT0kZeb7kOTjcTcul6FDLPI4ktDmLLerYbctxFI/LSNEs0BpavPtbVaxPIiKKqGbjVjyxhqN0IeS5SQW1JJ7Tt/YcWaT/nhExs47zcbeGp9bXaSPIuvJLKEfC9BUxUqSxIcNaQ/FcUFITlf+Pj/xxVnnkTN7mZ8aWIUnoLAwa5egVF8Nm7reyBE7+luCPSKazAP/kVgXVO9HL3/KcHAe/TPsN+aS/rlSxC+BXSo36LRpuZyz3lHsNsSVLd5pF2JiJpEdY24kcZZl2mtkvUqPfDRGTUsv/JhKn7yCEXfuZaGlx7A2rYhiJQnW4gePAq9MJ8cYzeKtwukh+/roGgINYKiRhDmIIRRBno5imJi5B2JOfQJhJ4PvoXRd1KH5ViXwnqoDK0XZ6g+CyWCtX0+eC3hdkLNRehlKFoMKQI7oejQX2P2/xkSE0Ur6p0IIfE9HekpyLZa3H8sQtGiQUQ8Uox+xJSg3vXDhzP6vnhvAdFEgqnnn54h3PbLmy/DVDVEjsrJU4/kO7OP4+SpRyJy1KzA4V7G54neqarAwSXl2Ti4n4tM7Pm+igjcAhrTklX1UZBeD+IjIoWgmIGHtGqE5EdaLb1mcAijAKtqj1rirTNRNB97xxqMvldjbbsjEAHrex1CSNKfTEGmP8bZ9TASj8ig+USHrsAYtDh8j+4I0tht9MJvZF5rpZeEx2H2m47vNmL2mw5aKc57vwz27SjbCLKq8jv+8nC3/gmEEnx/oSCMPCLf/DXGmMXIWF/IKyOpFf/blzJ5no+qKrz67ifUtnggFaRWiN7nO0glh8jgxUQG30fk4LuB3sd1ITQQGggVoXj4RJDCQJgD0PvNCEm50MsQkWJQ9N7FAqWH8+696EdMIXLKcpRh07Cbk/jpJH0n3otugh6P4W19OnO8X3srtO7K0BNp+stDxEVbNmKexZeGzgg53VPWveBZ1UulUeJxuvPo1sYk0RwD1U2heGkcGUOLqOjphnCb4kNGUrN+FR/uqOPM4cUcWhzjF29UsfC/t+H5/973piy+esjWkO8lRFSdR24cT21jC1ur68N61Lc/3sbtj73AzyeeTSUu0mlAaAXoRafhNK9DKzotsCLrrQaWwF5MiFyklodRMQmJhn78ApyNTwSRDqMApI82/McILcIWK49367pqxGK6wPIk62vSjFB8cvU21KxX6b8lPE/SpsaJ6lHqX7gVt3FXGCl3mnbR58wJSAnSSyGkh928Di3vKKSTRvgprLqnMYrPxurwSU5vX4jQCzHKrsDaPj8gGaWXYPSfgZBukLar5qLEhgd6CPihqnrna5ftWaRDtDCNOXAuPjkIxcauujdQmNbLMAbOC8hCL9eKTDdj/3VcV1u0FISK/dFj+Nv/FLbpIyZh16wOt5OpGqKapE9hgqdvvxrP9dBUhbKifOrr2wHZZbOc5eF7HaEbQIcAYaiM3i3lXFUFO5uawxKhTuX7ioL8f+If3vN9Sy+5k/J4BWlXMjwnGRCkaGmXeNuwyxF6DGvV1KCtdAzG6NnYa2fifPQE5temh2nrYZmE1dw7gbab0AoPQ+AhnepAL0TRsDb/rCPDpDi4troLrw2ci+yYoOqpj6Dj2G0Y/W7E3j4PP/k+itmP6NAV+HYVUksghIls3wWKjWz8MNg3WoqI9gnGHOkhk7twNz2NPvhc/NTujutGx3eawUjQmOou/pV94IWusV56bbjv/xb90PHgRRBmLr5Th1AiSCkQyM8Y1xWkbyF9H6QFSiSwhvLbQImgV96E/em0QADz/cdAywv7XZdY4CzsjR1ilW/c1OGWMh/pgqGksN+cRXpDTaiK79iNGZoAaqTrvJr9Dyf/hPPZtfSKz7zmssjii0J6SaSIIIXocpPwAvUVL5lCzSmm857iuS7Nje2YkSJ0pxkAW0YRChju7vA98waNhFV/5Eg+ZezgI/hk06e89/o6nnzfZvvOo7nrB8eiqwqe7VG9voGc4ggF/XPJIov9EdkI+V6C50kqCvIZ3LeYu3/3ZxZdfX4Ycattag0safCRXhMSH6EWoPc5HZwaJHrP9Nx+07CqHkSmNwU2KpuuBacaZBJ8F73fNwMv8teuwVozHaFFcPRKCmMmMV2QiAhOKrc4paydmN+MqYIW1Ui1yt49ykV2rubfAZ2R8r6TfkW/m16k71X3oRcXk3vEsfiejVAArwlr+3yMguODPtex3Gkv1t1mTC+5ICTjRtkEnN0vgF1FevP1Hem000I9hCBiNy3wBe83Daf2iZCEB0R8NrgxZDKNYjcj23djFP+I6NDHMQfdhVBNrKoHe6aGDpyL9JQetk0SGZJx6IoOdkeQVaKiOh6GVImqBjoaipK9Vf4r8FluAFGS4Tbd9TogsKi7fN6jpL2gLrG36HlMtRDpesrHzaHiskWouUXUPHYdRxbYHK3vJPXLy6n//x7CGDULpXQM+rAJ4NnYa7tS2P2a1TgfPhboDwy/EmmDWXo9kQH3YBRNQtoCrMZeI+cyWYcQNr40iAxdjhIZhFBMlHhQFynUSK8q3UiJ2f/mPcaG6Ui3GT02AIFAr7g6qFcWGhIdoZbhrFuA9dJFOG8tBCsQMews2/CVGNJNIlu3IoSCNvBMpGribno6EAvzHdyPVuDLbJS0N3SO9X0SCeTud5B2M7hgf/JHhDBwWjeA4DPuVXPw3WakvatDJFoBpxpFUcFrCv5XdcxBi5GWBKsFvWw0TvUbmMfPI3Lqcsyxi3E+fb7HvU3RNHRDxX5zj7KLdxZijLwh6Lej56CUjsFPd2XEFZx0KXVPzf2n11wWWXxh+EkQkT3aggi5355EieeEzQ11DaTbXHTdx+jwHg+tz2RzuN2niaPwFZ2Bta/z3qfbWbj8KZxUKxE/xWuv/BdTfv8mbS02byx9n/f/sIV1yz5k6+rMCdIssthfkGVdexGeJzFUjdqmVhb8+j+ZddlZFMRjJC2bkoI8JDZO/UvoZeNR9EKQPlItQHgNgUd5N5V1u3oZfvJ9RMm5QJe3slE5BREZgPPhM3uIvMxAO+E+ZDoaqGEnt+KsvQUnVYMZLeW4Y+bS0lr8/7N35mFSlNf+/7y19TY9+8oqiygKUVxQkcSr8UaTEKMm3ixu2ZQkqHEBwdwoAonKouIuIMYl0eQmNybRJHp/mlxv3AAjKiiioOyzT89MT2+1vO/vj5rpmWZ6FBQQpT/PwzNDT3dVddVbp97znnO+h9VPtPDZc2cj3pjzqVfLLZAfAYigju60YTc86EfmtvlRb7RKP8LT3fPej16Tm2re/RPI/m4NnkZm68Lsz531EIKjbkF1T/DNyinYDcu7FdVnooxSSMawX70NFXuL4CkPIt0k7tq7fMcIC/vFmViTb0LGn8P22nLuFfRiMB3Mz1zuC1S5aVSgFPfdP+V+71ANIlhJYPLtKLsddwAf4asAACAASURBVPOTGId8pzD29yMGbEPWJ4Onr15HD9uaYrieJGj1j54/ct1FlBR1Ym+8A7fb5tWcNZ3Gx+5AczK0/Wo6erSC6PjPIV2wxl9C5vkrsI6a1S+FXTa+AON+jP3KTTl/E6EaAifeguMIAsPnktl8XW+ke/AMnHefwRj7HYSM+foJ2Sj4HH8D3gCt+9wWlHJ9u68Fu58Ny1BOG8HRi5H2dgACg6aipEQltiMIomJv+bb96Otw40mMY25GL6mj0y7CjSsCViWRaBCBhxAaCg1r3FTstx9FtawuPBM+AM9TpL0IRcfOwXnrAYxDzseomACUYZacgFBOXlullCCz4YfZhRWswf4iex9bi/JQgLvjH1iHX4S9egEyto70+l8CEDz1V6iW1TnH4y8sash0LG+9uXJTZJ6/IqvQ7shwtruAXlT2gfdcgQK7i5Ip0IL0zUvviZBLV6KHex3y1vomUkl/vIXcJgBs6TvkhmGjSwdPM3ktWUz5iM9hvvQ7/qupgtrKMr57xql0dqVZ9qf/YcVLz3NVJsgZsQxHnnEQDWtaeft/trIq+Sr/2/IiJeFizjn+C4yvPAxd0/fVqShQIC8Fh3wvouuCjoTNLZd+kyvv+A0/uOkBhlSXsXzWdwkJg2RCI1J7IXbDLzFrv4dmhBGEyLw7068R335X/1Tc7t6z0NsiRSgH46DTsBtyU291ZWOGSzHdDpyVP8tx2Hn5WozR82ndFOefv4ZjzrqJkroAEp24HTrgBHoOVAxdUBxsQKVafGXznRxpa+h0UG53WqyOkv4DdOdU855UzJ7f8znrPfgpu12oRAwRHYawhhEY9jNAIBPNOKsX5qTUqkwrOF3I2Dqcdcsxxl+ONXFO9jhk8k0/etj9/8DI+QiVRkTq/LRj5SHNYvTBp6Dqn81pFWSv8cUPe6KFCVGHaxdy0fcXeroB9HUQjLK6nAyeHr2Ono4W/3nOZOrKIlRaLmndxOls4ZGrzqK5M80vfvccXZ2N2G/emGMP3bWLqDz9UlAeerSCmrMuxV27COetRrTJt/tOTLcY4M6ONyiMI6/FfXVenxTiObT8bRlesoOqr19DcORiwEOh4bx2F8ZBp6EJj3S3Mw69Am2BkfNRmS35SzG8doQeJf3urP4nS0mcxoew6i5GeQm/M4ERQOglBE/9FW57Cw2/mZ/Viai+eHnWzrseJNyoX4ahgTIUIV1gHHw+csx3C8+EXSAgEzQ9voTS476CdBR68UiUdNCUQtrb8toqa/A0oHeBPTh6MVKmEWi9tlboCJXBGnUGKtGWTTWHbvuYauqfwn7M9XiOwgiX4uYZs8LwnZsehXZr0mLqLroHhA668YH3XIECu43XEyHvY0e6a8iVp9D7tDxrqW8m3e2QB71WACQmntQwQhqm00mXUcHmpMaICV/C2/gMVa2v87X/uBbHVYSCAU45ejx/eW4l65t38NjwQRxTajLshBoat8TY9lwLW2sbeH3Hep5Y83cGl9byjaO/xJlHfIGSUCGlvcDHQyEPcy+S9hzOm7eUmx7+S7aF0g1Tv0ZVaRFSU4TDrSitxBe+0gykNBHC8FvWGOV5VaWdpkez2+9J7wWBCPZPvfXQePq9JBnXzrtKbnV3n0h3OhAso9OOHpBquQcy0XCKzKafZaPgOzvSTsP9oJd1i0KlwawFvdRXEu5JNe/+KcxanKZH/Si3TOc4633xU3YbsVdcg/38lQhPQ8ZbkclOhALSMbSysVjH3UjghAWIUA1uy1oAVLoNIVOoYBXKiOYt7VBeCmGGkU4C+5UbSP/9O2hugp8+8gpra2bQfMRdGJNuxXnrIT/CSW9WSVAvpGXuT+TrBlB9/iJS9LbH6anhPf24w1l+0ckMW/cIVV4DpttGiYhT+/pDqPsvZNi6R/jdNWczdkg55vhpaGVjAdDKxmKOn4ZVMxw9aFF7/g24axf1Oux2uz8G33kUa8KMnUohZmCvvQc0CzHmCgL/9kvMifPxVBFaqITqKRfjPHcp6ae+7bcZcyXWYVPRioeDcnMcbi18mO+giSAidGge+z8Pp/Wpge8p5bfEVDKNMKI4LU8irFLsV24Cpah/+D+zznhNn3Oo6xoq4fGPW1/lidkv8Y9bX8Xt9OiywwesgvqHwnNIvvEsO+6fztabz0Olu3BeuAqpAGvoBz7PldMASoJZi5Qe6KVg1mJ3vIJAA6FQ4UG54++oWThb/gfQCJx4M8F/fwRr8m1IT9D+zDKkEP2V1yfM8G15z35TjXjxRrYuOJP6pRcju2LUfO/2973nChTYXZRMoXZKWReu/7yVrkLro2PQ0tDkR9OBgNeT/SRwvIDfizzdzuakjkKQskppC9RwROdKlN37/B47YigRw6Jk+3tscjz+vKWDV9ve5Bn+j8PVIVx34qUsOGsmV3/x+5SFi7nlmfv599sv4PonbqM53iscV6DAvqKw5LkX6Uml9GsaH8i+/sK9P6W02AbHX4FWRg1CMxDuDqRZjTnocvDaUHqpH+1DRwkNhYlyfEORrR00SpFeCs0syUZveqJ/HbZ/eVOeQSTPKnmgJMyUOceD5vdULahFH1iEgxqasHMi2zv/lMk3cXbchjn0P/1xiEAYYRCCwJBLUEojMORSlILgqFtRXhdoRSCTvghRw0O+/kFfcarBM7BfWw50p0+mGnFeuQnzmOsgWIP12TsgE8NeeV2OaBEjz4TEdpwtT2Ic+g1wUihlExy5EFAooeM0P45RfCTKTiNcDxlbhwjVkHIUjz69ikefXsWQ6jKemX8hoo+YW8+xFNIy9y/69hDXlIsUBinCOeJSPTW8d/zwy7Q/voDKU7+djW6LUA2Vp04nFi6hbNKXcVddjZfqI2619WnMoadm+4n7tvN6RLA8ay97HHF79UKcdcsxP3M5WmQQKtWEs245MrYOa9yP8VJpnJbtaMEQMp2i9KRvYL9wRVYUzhx1NvYL0/3F0Ek3IKJ12Si4Fj4Mq/b72M2PYVWFsZsfw6w6m+DIRX7XDTQUOmbdRTj1y/rfU8Ovx96xpPuZUIbdvgKz7GTstx5FpdtA6NR99zaknUILFiGDxXhp/xwaSvCPJWtJtPlOWqItzXNL1nLyFUdS6Oq763ianhNZVnYSUTUBQRf2jodzr6eXQslkt7iljzBrUcJA6AZCuaCXIWUIFX8BiicAEk23sU5cDJlWlB1HaQHM4V/CXnlt7/idMANv4x+oOPUCnDV3gh3LHbMb/4Ax/PTe/YZq8Dr9eYUbq6fx4auombr8fe+5AgV2G5kELZLzUtYhdxQiFMrGzlsbmhl0aDUAAdmRVS+wiRAJJTCT7WySOngOz725gYpRZ1L+1n20PbGQirOvR2g6htI4NjSUZ+PvMjGk+PPmdiKZP5GItHN6/BQy73iYR+tMHPkZxlSMpr6jif/bsJIn1v6Df7z9Evd+ax5j60bvo5NToEAhQr5X6Uml7MuQ6jJMXUcTLkIL4jQ+BCqF9FIomQBAyAT2tsVkNvyQzLszUU4zymlB4BIYfQehQx8hOHIRCoVTvxwhFPbWZzHHTyNw4q2Yn7kcZZbxeocfAl/bHoRj5uWskpvHzqPLDpLBIyO9gjN+gBEOagSMFj+lOxvZnpE36u075feA7MDZcQcqvR6hbFAK5XYgnTYEDsppQTkN2DvuQiBRRiWBIZeCNbS7J/gjWFWXY7+2PDcl3Yn7TvnLcxEyjfBSWWccevs/C+WReXEGxpDPgdeBkCnsLTeQWn8+6XevBrsJs/KroJeiDAtlFmUXp27/8yuAf/8tuuQbbG7qLIgZfszoukZA0wmgE9B0dL3/40jXBSGSaNL5AMdAoEuX0uO+khvdzqajX9TvdXv1Qqwx38o649nXV16PMeaC7JZlbB3Oxj8QOOEmrAlXA2CvvonMizOyCz5oOrrhoDbeke3vLNxOv00aYBycux9n3UMoTxI4aA7CrO0WQ/QFEu3urgX21gWk1p9HeuNV4HXgbL/dT10e/BMwBxEcfZv/LBh1K+glBAZPQ5m14GQwtEGoZBLVvNpX4O6MgxBowSKcjiZEt+Cd/wXJOuM9JNrShS4Cu0mCEMGzf56NLHuJDsxDvpFtaZpzPVFgVOYRo0yS2XAZqbe+TXrDTxCyAbPuB6Q3XkHqrXNJb7gcvEaUl8FecQ2aFcH+1y/6jWtj+Ol+KvrhF2GOm4YWqUV6GUSoCmPs9/22dnT3lT9iJq1PP5D9Hm6sHk3a6EJCoa62wB5CeUkQgdwX3RRKiZwe5EopWhqa0aKDATDoyr7dURH0gIaVbuG9hA6N6+lIZvjciZMYde4c2l/7f8T+dgtKKawGl2NCQ1AoyjItpFzJe4kiPn/wCRjVgtR6B9WnNVpdSTXfOHoKs798KaZuMO2319OW6KBAgX1FwSHfi/SkUvbtZ3zPZeez/r83Iz3DV5N22nB23AmyC80ajCYUma3zdxLBmo9mlIHTglAu6Y1Xklp/Hpl3r0TGnyOzaTZG+cHYK6/DfuUmhFWC6wpi3RGQWFrxWqIWY/KdWKf8Gn3S7cRVbSEN8QAmGEiAU49C96NqTht2w3LMyilgDvIdaXMQwZ6+34Om4rT9A2vQRWjBESB0MtvvArcBoTJIuwmFhxYY7kfO9QqE60EmDR1bkR31yK5WBEFI+yloPdEc9x0/bVOlGrtTKUV+ISK7AxGqQSsahGaU5b1PhABnx+3YW+eihUuwJt1KMlHKeZ87gX/ePYsbpn6NBb/6K398aSPGsXP7LVIl7BAF9j750qRVwstxyrOtyZZ+n20Lv0rT0u8TSm3P2xM5EDLBMAkMPwRr4iys4+cgyg4FujUzlJt3TPnlPuX9XtciuanB5qizsVcvwF69AKEH/KgzfRT8hYa7ZsFOjv3srGMvzGjO/lXsLZx/LUTpVQRHLUYLjsyWivTtWgA9teXX+69vug6V2QiyA7vpMb9XuBKojg1kVszDfupc7H/8CPuFn6KFqgmceCvxN1YjEzHq77+MrYu+Rsvv56ISsd7zqEGkPDeVNFIeLMwOdhMDna5wLW2n/xzxvQfZ7FYgdGOA63ktArBqphM69NfZRRX7vVk7ve86hBAEhs0icNA8hFlOZtsNaEWV3b3HZd5x3TPelB0n889LyLw4C+F0Yr+xFCEdjMOm+eUVxy2g5amHyWxZ2/s9yuqw69+hfslFeK2baX98/oD3XYECu4xM9VNZF14S6Qm0SDSr9RZv78ROZ9DKRgGg671Za3a30rrlNrGhOUHbtnc55qBKioTN0K9cStVJ59L20n/T+ez9BHa4lBdHKY9GeW/bZkJagmDoSI6oOBx9sIZMK0RTfyNXW1zFj086j45UnFv/vnwvnYwCBfpTCAftRXpSKR/7xSXEYyncLo8tf62nbVOcl34Fky4YRGDoTDJb55N590qEWUtw1M351XVRvqCPWZ7371p0GIHJtyNCVbgZScw1CJuCpKMIm4JDKoLE0wLPs3o+tW9OQoH9EqEcv9+tGwejrFe1WaZRKoOSUfAasRsf8Z10wCw/GZVoxalfiTn6LKzqc8hsubE3bXboTKSStP15KXbju9R++3rsVddkU3atCbOwV9/k1+9GD0J2bc+m/EK3QFamHboj2z0TTa1sLMaYCxBWCYHJt6LsNkQgkv8+UXY2DVRmEmz+xTf8mtnzFpLxSigaMYglMy6kNZ7gR/c+xdQvzWBQeYSSaAm2iBYE3fYRu5ImPVDLs5ofPYBmyd50Wi+MHt+B0NtIb1/QOx6P6C6NSMcAmVeQTXZtxxhzAfaKa3JeV6kmzPHTEFYpIliBveaubDRcBUqxjpoJiKyCv3Az/jgfejrWwd/A92YlUg9ls0B23j/pGHRuRlkSJZzeUhG9NO/Y7tF10Kw6MlvnI5NvospPQ3g69mt39/tume3vIMpHEx47mfplP8qeRz1agRdvpigYwdMtXDPCaZePItOZIhWXvP1inMO/PAJX7NozIieLQTMP2PRmz1OUhSOEBg/zFf51DYU98PVEYf/rZgiWYR01DZADKOt3kt54eVYjw25YDii/jEcP9htXWs0kRKDUnw9YUbSysX5/8tULMcdPw141G4ZPJbHxNcpP+gY1Z1+JOuMyWp9+kNSGVdScvxAtGCE46liafz+PiilX0vTwdKovXk4XhQXLAh8OJZMoYeW8Jtwk0vbQi0vomZO2NjT7f6seD6zOccgd1a17Idtpe/c1AqbBxIPKcTI2mUScoWdfTXLHRpr/vpzaCROR48YwUtTyyoYNlJY4mJHjEbqBXumBDs4mCYf0P9ahZXWccugJPLHm73zvhHMYUTl0r5yTAgX6UlgD38t4niKCxcuL1/HqfW/TtslXSd/2eiuZdCXSHO6n8x76CNawWSinLb9gj9BwWp8CtLx/B4UKlGN3Jf12phImDQ5y6ogwkwYHUenMXk1LV1JSpKcoFp0U6anCavr+TrdiutNwP6CjBYYjzCq0wHAQIfCacBofwar6mi/kFjgYqarAqsM46CyUa2M3/Q5r6HRChzxIcORClNBBBkhtWEVmy1qaH78D65heQSGEhkq34da/gPSc/pHGideDFUXa8awQkVY2FnPsRTivLyb9zPlknrsCJSIoYeW9D6TTnP1ds8IMvWwZ1WddRezvyzASG4mmGtF1wQ9u+iVPrniDs2b/muMuXcqpV99Hwrb34QU4wNmFNOl8Lc/0aAWmasN74TLsv5+L98JlFKkdCLcNp3FBbnRx+0LMsRf49d/v/DavIJv79kPZaHhfIUEAd/OTSNuh5W/LoOpLBE5+COvE2/C0EkSoFhGsQkRHIFMpUJLAZ+/CGHUO9hvLkF2bUXYcTTqII3+O1CuxJs7tv/919/mLBm44K9omjJL8z4AeXYfMFr8FplkL6Q68VBfmkdfkbNsYN532FY8jJYCWPY+BYeMoP+3HtPxxPlsXnEnT0u9jdmwh9pcFtCz5Opm//YSJXwlhFOu79LzYnSyGAwHPU5gYhHQLE4NEughhlg/wTNd9pzodw37lLoRRMcB19/vG97SMNGsuQCmB0/oGSoE1cU722ms1kzAPOZ/Mi7PIPHcZmRdnYY79PtrQ07MLoYETbsIc9hlKJ38VGd+MSreg0g2Uf+ECas5fSOeL/4Vs30rlad+j9vxfYJTWFNqeFfhoKAky3T9l3Y4jbQ+tuFfZvKXeb3MmBh+HVBqa3lte4yg/wm7RhdvVxsmHD8Vz/SXcdGcbwZIKohPPRtMtdmz7LV1lMKK2FqkUseb1gGBDQiB0gV4pSG9yUSr/4uEXD/scmtB49OU/78ETUaDAwBQi5PuC7pTAvhPQSHkQT0E8GaA45CFIktl4OVr4MALDrsmNPB40B6f5CayKs7A3/InAQXPIbJ7dR9BnDrKrDUJV4GTwQiUElEG6K/0+B7Xn0HWB3biBpgevwo3V96qyhgYfkJGSTwIKA/RSrKqvYdcvwaw4DaGXglkJwkAzSrAGX4InLRIpq095g4muC8I4WNXnk9nSdxzOxeloZciP7kQpgd3SROvff0v5F29D2DFkug1rwkxEsJzMi/7PwAk3odwUwoxgr12SbUFmHn8Tgcm3goLM81fkpAI7L83CPGEhgYPmkdl0be/+h/0Uu/7ebCRJOl24L1+VFfbyZIjGh6+i8uJlA/atNgslk/uGAWwiGhhCoNJtGEHBkEuW4mUkQhN4yU7MkpL++gIrr8M68Ubc5jyZQ8XDsFffjWx8Aadrc2/U24pir16ASrdht9YjxkzHLCnCXnV9jpCg0+USPeYMtGglXaoMmVFErBRSKpQeRMu04L02N9vP3DxmNuboc7J9yXs0DJr+vIyKKVchRl2KESlBixTjvLEkG3UXWgjHiWINnobE6j+2h87Abn6MwNCZ2A3LusURZ4I5GByF1DSs4xfhJVrxOttoefoRghPPoT3mUVFtZcXGSk+6kObfz8vJOmj81dVUTLmS5JvPZkW9qi9ejr0L0dCBshgK0VSfjC3RtVKCO1/PwTNRmTRoBoFJC0BJZKoz/3Vv6E2bVU4DWmAIMu1gVo7Hfv5yRLAc66iZiFA1CM1ftOxrLzf+AfOQ87BXzcmOSfP4G/0U9tcX9xGDm4lRXOOLH65dRGZt9+tHzyZ8+EkFfY0CHx7l23m1k0Mu7C6kq9ArirKvtTQ0YwWDmOVD8DIhdD3Tuxl0XE/DDEBtWZRRJTqO6y8cpjraKKkbjhEupaL632lpeJKgdyXVpaWYhoad8AhoknWdcFgEjBqNTKOH1wzstE4AEA0Wcczw8Tyx5h9c8fnvEzKD/d9UoMAepBAh3we4QjF56rhsnV6kPMjkqeOyyuYJO+z3E+8W0LLrl2ANnkZw9L0ERy8GrRir8ssoV6BaX8dZ+zCB4TcRHH4bwZG3Yv/rNpxXbkA4cWLPLEN47j51hEMkaeh2xqF3Uhai0EJqfyWdCfkp60InMGiqHxk3K3FkMbFOi9bOKG2dETq6zH5aA56nSFKKo6oJjPDF2gIjb0N5AdTaX5D53+/irJyJGRZ4yQ6Ekr4oGxJn3TI/Op9q7E6jXABuEnvtEozhpxOYfDuBSYtwN/+N9P/7th+9yVMjqWkGzpoHsSqmERrzoJ9dolysuouxBk/Dbn4MoZl+a6tgGcJ0sCqKqfr6VeiayCu2aOQRFSuwdxjIJipDEdXb8OJvg9uMHrQxS4po/dsdtD5xC0ZJ9QC14PoAmUM65qiv++npsXU4a+4C6WadcWP81bT85W5UJp51xnu2aa+6HrOsAlE6CBEMEtE7KbE6kWtvw/77uYjOd7BXzc75DHZH1hnPbmflbKrPvJLWx29m+9LL2HLrhdQ/dK0fdf+3XyJGXYrUy9HsDB3P/wWUhtLCBEbd4tcXj14M1hCswZegPAuraipWxTTs15bhxbax457vIztb8UQQVysmKYrJHHIaSYoJl5ST1iPZ1nF6uLhf1oEbq0cPF+f8f1ejofmyGArR1FySaYmnKrCqLic4/LbstXNeugZSLaSfuZDMi7MAg4waRGDEYgLDlxEcvRi7+bH+SuzKQAgjuzAlY+vIPH8lmeevAs/pd38Yw0/POuPQPU6Tjdj/utFPlz9+DtbEWSjSCMPA2/JY7vj91xyqzryy0PaswIdGqFT3L7kp63hJpKPQIn0c8vomyuoGIYRASiPHIQdoTRroluC4kVVZZxz8CDlAsTmYqqp/R0kHuXUFmhAEily0dAmDQ4INHR6g0KsFCEi+7TAQJ446hoSd4oV3//XRTkCBArtAYclzH+B5Ej2ic/IVR/opmTu1GbNtiTQDfuR702zfKd9+F4Fh1yLdFPama/rXRWaS2C/f5EcYuxWr7ZXXUXrcVPD27WSoMCn75JFMSwhWEQqEQLkgDNKZIv/1XcDzFHHPAAyilo0hu3BWzeqnbl11xnyQti8w5MT9FHXlZuseZWwdzrrlGGMuQIsOBTTsNXf29gfv7gHdU4duHPwthFUKykM2vIDd8AKi7FCsI75PZntuazVn7X0Y4y5BGDaZbTeidvh/M615/Pq6izh3rh8pH1Jdxn2zvktQNwsZHfuIgWxiyMgg3FYyzYtzoomVX76E7ff8EKd5S95acKUszJqrs2nrPWNAueCsW+ZHxs0oSBeFwjrqpygMnGQSvagCq3YEmbf6O/rxtmZ00qi3bslpK+XYMYQR7Of85HtNpRoRGiTffDb7WmbLWrYvvYxBP7oPEaxA2Wnc9gbiK/9E7Kl7GX7dX0H3QNNAeYCB884fkRv/K+d7e10d3VHt6dRMXU5aWahwOSXRWoxgKY5SeLbE624dp+PltOYCX8TLS3bm/H9Xo6FSM/NurxBNzUWTGewXftr/D5EhdE1cSjhUhC2i2EmJrnlsW/Atyk77ISXHX0AmvSE7ps3aWTgtbWgik39hSjP73x9Wad5xSrCsv90Mz8EYcy6uHctqe/jb1Qq2scCHJ59D7tlouEhXoIfD9Ki6tTY0U3PYUQBIV8Mweh3yVxpchhgmx5ZrHGJ10EHvwnqqw3fIi8KDcMsEmhUhvWkVoZEnIwPtqPZyilWSd90IndKg2PTQKwRdG9KEx+e3V2OqDyIajPC3N/6Pzx9y4p48IwUK9KPw1NxHeJ7s7emax+eRUiLfeJjAuJtAOQgzikw0YTdc068uMjBuFsIs8tvZvPPb7DZUqhGjuByvezK0r8R2CpOyTybJtCSZ9qMeVVVRkh3xD7cdL0CJkRrAERG4Hc2IUE1vP+dNf/PHbndEUqXbEIFS7EQGM2hknXHo7QHtbPwD5qiz/Z8jzwLAOu5G3Lcf8iPtry3HGns5WnQosuPd3tZqI6eQaVjcT+F46Ig7+MMvpuF6EkPXCs74x0A+m6jrNpnNO6nnb59PYPhCAFqeXErduXNze9RPnEtaFIEYgj70ZgwDhPJw1izFHP1NPy195XXZ/YpQDfrh16CClWQC1ZSdejFOy/a8jn6wKIp6+YZ+baXM8dPyCrUpN51fPE5YDL74dvRICSIQQTppZLwNvWwQSSIEZIKOF/6Lqq9f66eUdzRhmi6Z1fN3SqHfli3rMMZNp/GxOwB/AVRIF0+Pgg4u4Lq9XcQ9T9FFCF0XVJ+/KJtm7gseLiD2zH0AveVGhNkV4c8U4X7b253PHyhIYeQdFy4mrhHGlmZ2gV4K/3kae+pekuueo/y0H2JWlaNZUZr/dCfRo6agmyL/whTKt7HdLfZEqAYRKMs7Ts2xF2Sdcei+1zbPJlA7C2vC1ahMO8qJ425+EoGgSE/1m0MUBP0K7ApC+g65Embva073ImCwOGsqMqk08fZORgwajSYzSBc0Kw1Cw3E97vlXhkuPCKMHuhgiWnMccs/OkOnsIFBTQXv0LYyqUSQ2v4KlHBLGdgTluJ0tYETYmhIcHgG9WmC/6RGJm6ho/3GrazoThhzGcxtexvFcTL0wpy2w9yjkaO4nJOwQ5iEX4qy5D0QRMtGOFirLr84agC6jowAAIABJREFUrkIaEZxt/0BufTL7NxGqQYtUkCK8T8V2UoSpvfDmbP/V3ElZgU87nqfwtEDevt6ehKbWBN5h01HpNj8aXjUBFSgnMHkxwc//isCJt+Lp5ehmEOXJnO309IA2P3MZCA3r8Kko6WK/cgPO64sxx17kp6WnYwizHPvNX2O/NLu3z7kZGkDh2M0RXypMIvcX8itNC11j0NQllH3+B3h6GdbxizAmLobhU2n6873o7dtx9Qht6TAtiTDKAXPk13He+Q3W0f+Z28LsyJ/S+syDSGFgeQkaH55Oy5NLMcZNz3mfd9h0DEMfsK2Uv1g0M+czhGv7ibeZx89HOJ2ojXfgrLgUZ9U16CIJzX9Fk75gV1KFKTt1Kh3P/5aqr8/GKqkEzfCFuMrG9qbQHzaVwMkPoY2dQeNjd2TbVe3qAqjnKVLd0fIhV/+Z6ouXk4kOo/QrM7P/3x3tj3zbK2iH9CdhhzCPndevzWLKCfezPz2LHEZZnS+O+fubcTvSNP/xTkpP/Bbtzz5I+4rHsY6dk7M9a+I8hNB8ezl+GoETb8UcPw3pxPuPU6sYrWjwwPOLRAPOm0tx1tyFeegFOLHWfu3PCoJ+BXaZngg5vQ55b/vTMno88pZuhfXA4LEEnBakZ6CJNELT+MN6h+1xBeEqNENQR8tO+wC5NY42qIj2UBtG5Sjs9gbaW99EGWmCAZ3G5iZMDbYk/P3p1b4L5G4b2F4dPmgMKSfN2h1vf/TzUKDA+/CJWu6JxWJcffXVbNmyBcuyGD58OHPnzqW8vPyDP7yf43oKVytGlZ2KQGC/PAfr6Kv8mrE+D01h1oKUCKEwh56MXf9sTqSoS5Xieb7i+b4S2/E8hVUzmuqLl2dbEdl6hJCXQBOFlfMDgYQdInrsPJxV12bHozPmJ9x7yTlsem0F4085kwt+fg8aCikMEulQv9p0XyyuHevYOdnaXBGqwTjsou7a3Pm5acPrlmOvnk/gxFtBgMREtazO2aZyUnnvIaU+UabvgMFTZt7r5bQ1smPJ1GxEt/WZ+3JSwO0db1MztVuITEHcKycSLsIafwkKzV/0ScRwY400P/kApZ+/mBRhItJP+XZj9TQ+dgcVp07FKCpBRCpp2bSJqszW/JHI7vILqSzEmCswghFEoAgpA0hRhD7p9m5baKI0gfPcJXmj7Paq2RRNuhXX8VBFFZSd+VNM1ZYVMuw71mVsHWg6XV4JlpbAi7cCux/V7omW974gdxJw2z073W97hch4P1xPEddriWTHhUHc7m8DIXeRQ1MuQtNQQqPsjJmg6VR98wakMOjSIhR/7i48J4MUBp12CNIQPeQ7OXY4MGkh9po7/PI2O46y23HX3ol5+Hfz3muy412c1+/utbErZ6ONnUHJid+g/emllH5lJl2ECoJ+BXaZ3hryXvU01fae/1K0Ivtaj8K6NeQIAnYr0jMQQtKW8fjNGzYnDNao14YAb1JCe84+Ioko2ntpxOE6wcpyMm2jAejY/BKYUF1WwqaGZg4eodic8LNRtJDAKNGwN3uExuZXdD20ZhRCCF54919MGHrYHjkfBQrk4xMVIRdC8IMf/ICnnnqKxx9/nKFDh7Jo0aKP+7D2GFIK2lc8DsrzJ21v3E/goLlZsaKeukh7zRKEdHHiaazjFxA4+QGsE24mpQ/C7u6jvK/ruoWm0eWF6JRRUoSxurYVVs4PIFxPEVe16JNux/r8rxHHLuC/bp3PptdWUD5oOKf/aDZdThGxdBEdqeCAE1EhTDyjIicC6nW09lfWXr0Q4+Bv+SnvqUaU6+C2bsM69vqdIkE1mLWzcu+h4fNIpAsTxv2RRCqENfznOdfLrLuG5sduB3pVwaNHT8n5nBurR5O94jyup+hImrQmI7QlQ7RninCNCrTKMZROmZmN4vaU24Bf173j/unUP3wdyoWiaHHeyLk1cS4Uj8E8fhEtTy6n5S934yZSbF9yCVtvnELjPd/B62ij04mS0UuwXXvAKLtKNeLFm9i28Ks03vMdDJEecKyLUA0Sk5CexCwpZvAl9zHkmr8WotKfEFxP0ZEKvq8N7MHzVPZ52uFG6HRCxN0QKS+ACJiYpkdIT0KgNGd7O9th68TFoJmodBsq007mucuwV17nl/m8cT+BwTP6zS+c9Y/2s7FGaTVC6FScfgk6HrouCtoxBXadnpT1vnLmrZsB0Cp7xThb6pvQDR198BEEnFY86UfU/7KhEwV841B4I+3b65Doyn7OcEyqWmpJNjchpUfx4GHoJbUIM0im/k2CeohBZRWkbYew00F9QuIJfz5q1epk6l0YQNstEggxomIIz28sCLsV2Lt8osJEpaWlHHfccdn/H3nkkTz66KMf4xHtWWw9QvVXpqLSfs2tir0FqgirYhoiUIzKdGZrY9XYi9l+5/eznzXK6qiZuhy6V6b3dF337tSKFVbOD0x6Jpzgj5ezZ93JmTNuRdNNCJTsksOgNANhJ9m27MfZ8TP0smUDOjQiVINy02hBDbXuJpxgOeZnLkeLDMJurUd6Fq1PPEzJpO46zGApjoy+72S4wMeH6ynimVpKDr4bz82gIai/f1Y2NRu6VcGLclXyjbI6EAOvLw8UxU0Rpub8RTT2rYH+jzm0/mUxFV+ZTme8NSdyLtMpHFFOPGmh62FKp8xEx6N+6cV57Z0rgqQ8g8gAUXYRqsHrVgd2Y/WodGf+sW6VYk6cB14Kd8XM3jZrx84jSW3BGT8AMHRBVDTgvHBt9vobx/0cQ6/JsWd97TDgi26Ov7qfvoGKvYXz1mMEDl+AMPRc7Q1ybSxK0PS763t1B86/GVFUVtCOKbBL9ETI+9aQk6jHCwmEGUB5vjfcXN9EaXU1mmFhOS14rv/+9zrifGlUGUktzPa0nxEb0FNYmsB2FDVNgwDYUbQJdgyidNhoGl57GaNkMKJlG5Vjj6CqpBQAN96KCpfSZGvUmR6BOoPkegdVLxDD8tvRw2pH89c3/pfOdBfFwaK87ylQ4KPyiYqQ90VKyaOPPsopp5zycR/KHiOkJ7FXzcZ5cxnWUbP8B2GiHue1u8k8e0W2NlaEanDacmu/do4Q9a1Dg49W1727tWKFlfMCnqfwjGJUoALPKN51h0F6yEyCiilXMmjqEmrOX4h0nLz16cpNY02YCaFq7LX39LZSW3ENmRevBs/F62wmetQUjJIROK2duCmPpGMOsPMC+wOup9DMctq7inDSWjY1uwejrA49UpZr286ZjdStfJv7YEyLyjNnMmjqEirPnAmmhdfVitJ0qs9fhBdvpfXpB3DiCbSSISD9CGFPFFNJObC9U4qGTLhfXbk1YQbu5iexjp5N69MPZD8njGDesS6CVUizHGfFzNwe06uuJWKlKPDpJ2Klsqno4F//zIqffeD1T3oBPL0U1wn2qzvXh55F/YPXkdn8Ds5rd2ed8Z6/KzeNdfRsmv54S27v+oevwmvZQtXXr+03x7D1CEV6CidWT5GeKmTGFeinsu617EAnhdKjyD5dgVp2NFFSNxQAy27B8/xn9YhKmy8e5LHBraTVLUIp0C2NYlJUtFUTzIRoLNtBRti0vvcORiBI8ZCDECV1hDtjVFjVhAIWxZEwba1+7fn27kMyKzSECfaWXhHMnTms7mCkUvxry5o9fWYKFMjyiV3KnDdvHuFwmPPOO2+XP1NRsXdXtqqqoh/p8zKZxO1OwXXeXOor+ZpFWBP7qwrbsS4Cw8bliPoITaOqovcYlDyYwdMeQLk2wrDQI2WEtN1fg3HjrWxf2j/iPXjaAxjlvfU/Pd/fjdt5V851K0BV9KOdo/fjo57//W3fe3u87szHef56cDpSeFLS+sQtOSrQ5pHX4Lx6Y597YA7KiOLEmjCCgRxldvAnq1bVUNJNDbQ+cQuDpi4lMHgseqSM4Ie4B2D/OD992d+OZ0+P16qqKEpGqL3gZhoeuio7HmovuBmlG1SeORPNCiHtFFpxJVZ096+tb9su62erKs+cia7rWHUHM/jSh/Dam2joE0WvveBmrNrRCE17X3tXFDQ42I3jJATmxPkoO4EwQ3iujXnYj4j98/e50f90BmPcdNy1i3o1FMZNx3MFhung5omeG5rc78bCzuyPx7e74/Xj/g4984O+7Or1VzKMl4ghpYc16Va8eBNeZ1tWGLB9xeNUnTEXp+8849jrcTq6QELyjWdztufG6kFA25N3UzHlSvSiMozywQhNR+vYlnuvXHgzVo1/r+xPfNzXc3fZnfG6v30325M4nVBcUowQipbnnsMIa4jSOkIh3w1xbIf2ljaGT/oCABHZzltNISpr4dQxEOoUbEhVU6GnsD0DPWBTHbfJdJTTWRIjFY5joZFubSAT76B23FFsfq4aY6NkECYBS6e2vIRN9c3UDJfsSGsU1fn7DtQZ2Fs9qqMRhNZ/AWlC0SEE/9fiX9vX8B8nfmHfnTj2v2tZYO/xiXTI58+fz+bNm7n33nvRdsPIt7Z2IeXeSe2rqorS3Pzh2kb1UBLScvoz2yuv86Mj467FPPZGNEMHYSCFjpDNVJ4xg5Y/L8SLt1J9zmw8zaK93zFY/j8XSCc+1HEVi3TeCJBnZ4h176/v99f1QN5WOJ1OAO8jnqOB2BPnf0/te08Z0L05Xnfm4zx/fSkxJI2/mpkbjfnV1dRN+yWByYtBuiB07I42Wh67hsyWtQz/6WP5hbekQjNMqs9fRFxF8dLqQ98D+8v56WFPHs/+OF5z7ElwUI5gZKI7yydUPcaPQne/1tmawNAFESvV+94BhLN6GMi2mZXDfHvVmqDYcLIORs/fGx66ipqpy4m7obz2rub8m3EleK070HSNlqfuwetqpfSkC9HDxUg7hagbQ+TI00mseTr7OS1QRMtfllF6nJ8i73V10PL0I5ROmYnStZxxrpWNxRhzAUgPu6v5A7/rx8X+OFZh98br/nD/950f9NBj51JtTbsgnOpHJ3VdEJadND12c3bclX1+Kk5wGObkO/22q04Gme4CL4MWrBuwd31my1oaH55BYNg4Ks+chRdvoeWP89GjFVSfdRV6UQky2UImXkHc/pAZLHuBfXU9P47xuj+M1Z0xu9oxtRCdnRm8ZCep9SuJHhnE0aM4KT+zs2HrDpRSBAcdguHGkXaSe1ZojBlhMbbWYV2rZINdwVHWVlKuRSiQprQlwVYzQ314B9LtPTdbX13J6M/+O9HxJ9K+8g+UdnaRKfWoKC7m7a31hJ0ONncadCUURZEAqkzhbZG0v5dCVOb/DiMrh/P826/s03O7r69lwfn/ePnEOeS33HILa9euZenSpVjW/mPg9wQ9StXu+gcwhp+OsEpRgQpItuCsuiZHdZfmv6IP/xrV37oBp+ldtOJKEl6AvaFwu7v16DurxEphFFTWC+wa0u3nIOnRCnQvRubFPtGbCTOpOedqWp5cjtLCBCYtQmVivoLw5ifRR3wdN5lEL6mlSxUVxt4nmHz139k0WKWg+9d8NbbRY+cR12sHdFQHtG16EM/19yOknT8lXTpAqJ+9U5oBToLGe76DG6snfPhJVH31J6h0HLejmda/+VHJYTP/1N9O6hFKP39x3r7ewibbyUAEyzHHXoTdp0/5B33XAp9s8nWyMMZfTf2Ds/xF+fMX7ZK4n+cpkqHcRa4kYby4TcAKElFtORl52lE/o+a7i2n85eW9Y/Kc2bQ+eSeBYeMoPelCrNpR1N83jer/uB49WkHNWZfirl2UvQ+tirkYel1hbB6gCJVCaCEUCvvNFzFDvviwMnsdwJZ6v+WZMfRIgnYTrzbAq/UanmsRiqbY4paRwWSk3kjKNSkK6IRlC00ljcid5r2djduJbX2Pw08+l3/+cgbB2A5Sw6C6tMR/Q7yFZr0Mp7tqV68SIPz2Z2Zl/hKLQ2pG8Nir/0Ms2UFZuGRPn6ICBT5ZNeTvvPMOS5YsoampiW9+85t89atfZdq0aR/3Ye0xXE+R1Or8Nk/CBOWhKQfn1Rv6q+4OPx13zQJ0S0OrHoNVMWyvOR0fph69r0pslxcqOEQFdg2hZ8dZD5WnX5xHeXo+mmZT87Wr0Ukiu7bhvHGP3zd3zHl4bhAtUFRwxj+FDKRpka/G9oNqrAeybUnVHYUnCUr2G5M7i8gJQLcEuiUwTEXsyTtxY/V+5PDUb+OsnImz4lLYvISasy4lfPhJCCPQz07athywr3eOgvbR12ad8V39rgU+2fS9/oHP/wox6lIa/3AbmS1rs2VkIZK7tK18z2dDFxTpHf1srfPKzzFDFlVfn82gqUuo+trPEFYIvaiC8tN+TOsTt+B1NvtZc8lOKk+/OFty0bMNe+V1hbF5IKPSoAVRnktmzT8J1FWghI6n9S60ttQ3IoRAH3osIbuBR17TKAsqIpqGaaR406lDQzJMNJFyTXRLELIaiBudeXe5ZfVLpN0MobrReN2K7gHLpLQoQjzm15E3ZnwbLkyBXiawNw9cRz6megQAq7e+sUdOSYECO/OJipAffPDBrF+//uM+jD2KrmsYSoAENAgaaUQyhv36YlSqkcDk29+3ZQ5IurzIh6oN31UKEe8C+wqpW1SfM5um383JRmPMqiFk1ua5BwIVZJ6/vH9v8pfnYE26haQqxkvLj+mbFNhbDNTFYcglS/LW2L6/mKTAiwyl9uLl0B3dTqpe26ZJB6e9sd+YzIrIefkj85WnTqcx3krFqd/p55y4axdRfeYitKJyyPQvoXi/vt49Ctplwa68zwUdB10vLIB+Wum5/sXCZvvSy3L/9hGFUyNWCpVpyzuucDoww4LYC38hfOhn0QJhKs+Yzo4lF2cdcaOsjvZnH6Tu/Ll57XVB1PXARcgkaEGcd9eg4jGs0mEoK4qSvc/n5h1NlFZVIgJFbF3/Hqu2a3xvgoPrGhh6J2ucWsYYTQRxSCUqEKUtRKxmNE0gVX9752RS/Oafd3HiiCOJrX0WIfyEqqrSYt6rb6R4uMe2lMGh3e/XqgTOeg+VNBB5Yk0jKoZg6SYrNr3GKYdM2ktnqsCBzCfKIf+0oesaKuHxjyVrSbSliZQH+co1I8n0jXzY7fnrxnpa5rBvFKPfb5JYoMCeQNcFZDoRVihHsEuRv3ZSJnb0yxwxx0/zIzxSYnd46BEdzys45Z8mBuriwEDjZIDSmh77+0wf+zt56jhERNBj36Rm0vn8byg95bs5Y1JEK0h2lwhFrBTOC9f2c7orTp2KXlSSd5FAadpHErmSwsj7XZ3GTYSCFYW+5J9y9nRbU8AvtxhovmG34669i/JT57P93mm4sXoG/ei+7P7bn32Qqq9fS/Pv5+G0NezWfVjgAEClEHoI+43n0YpL0WUcL3AIfeeRLfVNFA8aBsAfV2yjyFJ8YVQax+kWXgvaTJCbQUGiYwgMXk/Asik2HNqd/mOr3Yvz6qYXmVxxNE57I2FpkxEW1aUlvLOtHivdzrZkdfb9RrWGs14it4E+pv9XMHSD0VXDeXlzQWm9wN7hE5Wy/mnDUILnuieDAMFi37m2jpqFNXEuWtlY3HcexZowI3/LnIlzSdiFvt4FPh2ESNJ4/2W0/HkhynXQi3wF/7b/uR/jiJm598DE63F3PIs1cS6BE2/120oFy7N9cztbPJ5bstbPPinwqaLHGemLUVaH45mYx87LGSfmsfMGtJE7299EWzo7ZnRdUKSn0JRDxZQr6Hr9GZTrgKZjVh1EJlDdG0VXbv5odXE5XldH9ni0srH+eJ18O0JoKPXhF4oSdqjfdzXGTaflyaW7lbpc4JPJnmxr2oMUhj+vyDffeOdRX2w21Z51wr2uWHb/mS1raXvqbr9tYHQo5jFzdrLXc1GeoCyUpCzURUkojVFoh3bAIFQKJXXcresJjT4YgYcye+uwXcehtbGF6JCDaWhs4vl3M3z1UI+ALsl0iwGOiTRxsNiG8gy62ocDoAc0yujKu88WNwZAe3c34GL81Paq7jpyPdHCtkSvDRYR0MIC533an42pGcHG5i10pPYv0bwCnw4KS5YfJ5LsZLDioCifPTdK5vkr+qXgOhv/gHXcfGQmgR4pQzo2quxUXEoKIikFPjX0RD71aAUiWIRUEs0MktqwCrvxXarOmIFZVgNCR2oW5tAv5IhaWRNm+tH08dez6uFG/94qBMc/dfQ4IzsLnyW9AIJaIpNuz5bWxN9PebyP/e0h0ZZGE/g16jmq6YsQkTKkhC7CeHbvwBooWq1Fa1ChWoyauThvPYg56mzs1Quz4zVw3M8x9Jqc49N1QYgkmnSQmjlgaZDrKeJ6LcWTbsXrqMfr6si2sAIK6cGfcnrKyAZPewDPzuyRMrKEHSJ6yHdw1j9A4ISbUHYcZbfjrFuOjK3zM/I627Lv7xsVd2P1ePFWtEgldtIjkQpRcuIdoByQkthzv6fkyMk4fdr5FUQIDyBkCi8pQSnMiiJIgDQi2QB5845GlJSEDzqCvz3/T0xd8cVRvm3+v9gYRo7YxumVG0jGFW6iDjdVBvi9yMvSHbxHab9dtnrtGOhknAAAVroZQpVYpkF5cRHpjhbSVYpkt/8thECvEmS2uQRcDWH0XzA6pGYkCsUrW9dy8pgT9sKJKnAgU4iQf5xoECkPAjBhSiXijTn9xdvGXIA55nyceBeZpgYaf78Q6SjaX3ocWXA2CnyKkJpJ+LCTKD/tx+BmaPzlT2h98k4qplxJxRcvxU0ksb0grckIyvP6iVrZq+cjjSqefbiT1k1x/94qWLhPHX01LfIJn3WkgsTSRXSkgu8/2e9jf3uIlAcx6epXo9748HTfGc8jUJkvWm0eO4+4HaHTCdEp6zDHX5p1xsEfr5kVP8sRuhpIrE4fIJLoegrb1qh/+Dp23D8964x/1NTlAp8MPE9hRCv2mHBqj2icNu4nSC0EegBnzV1ZZ9yaOJf2FY9n35/ZspaO539L3dRlVE/7b2q/dzftf19G851n0vWHH+O2t+E6Otvv/iGRUUf001IoiBAeOAiVxo3F0EoqMbQ0SuhIEcj+vWGLb2vloKN48dV1/NtBkogliXsBft10PI7UGFnkC7G5nUNw8XClhggalHmxvPtsdWOU6yWIYDFaIILbvAnD9O1iVUkxsVgbSnrs6DMEtSqB8kA15re5B1UMwdQNVm5+fU+clgIFcihMVz9GXKGYPHUckfIgoajIL94WqqX+0RvZfscFND48g+Sbz+IlYlRMueIjpacVKLC/kSJMxZQraP79PDQrhBurz/a53bFkKg2/vBzhZgAQA6QJ2wk764xPnjoOVxSiL59G9kQXh772F8iOGVT/1nvvJ5iVo35+yq/RJ91OXPVG/lxPoaTMO177bnMgsbr3Sz/fG6nLBQ5csgtaSX8hqe+Y7hKDKP38xTljrfTUi0lpUcxwkIb7f0zyzWf97cTqaXz4KrTuloF6UckHjv8Cn2JkCifWhjn0YIQdQ1llKNmbGt64dQdWwOLlBkXG8fjS6Ayg+GP7kaSlhW2bhAJplGshk1Xo0e2kXQsRtCh2WvvvTila3XbK9VKEEGildSS2rqOyqgqA6rISpJS48Ta2JPpkKJUL0MHdmj/aZXbXka/aVHDIC+x5CsvoHyOeJ9EjOidfcSTBQBI3n0hPy/Zs5AP8h6DXFUOLVhdEewp8qvA8hdJEjmrvQKJFA6UJB0rCTJlzPGi+w1UQdCswEH3tb0+XC1coJMZuC2b1ODK95NrmgcZr320OJFb3fk5LoQNGgb1F/zEt8fKNNVsS1Lz8QovdbSx7tBQKQm8HIMpDkEZmXIyaIWj2JlSolr42smFrPRVDhvK7la9wSG2AEaX/v707j6+ivhf//5qZs2ffSUA22aJsQYQKVRS10BbqUqnoLd+qgKV669XrAhbr3gWx6tVSvbbW/trLVa+tWBELFXAHWSRUNgHZSUIge3L2M/P5/XFISEgCYUnOCXk/Hw80Z5mZ93zmMzPnM/OZ96eW8oiHT2v78c3EHURCOg6nn0hdHqCjJxwgELGR4IqQEDyCntA003qNVUsEkzQ9Os55xJ2Od/8WzsvK4FBxCZkpydEu6t4y9tXmMSoxOp1maNgydYL7TOyjNTSt+Z3yAdl9eOfLFdT4a0l2JzX7XIjTJXfIY8w0LYKWSV3I1bzb46gnICm3yRXprBt+Tu0X76JsTlLcAdJc0QQpZ5IgSIh4YWn2huFzsm74Oba0XJw9B9P99ufpcceL0XGeDa3VbsJ1IRdBTIKWKY1xcVIaCpfTi8dVjcvpRUO1y13nluqrc/STTRLOtZas7mSNlrPRW0CItmitrlmareW6azjInvY0VWsWYxt8X5sTLp6MzdCa/P6RBHFxTEV7+KgwGKnpaKYXZTt2LFWWRenBEsjsS+mRMq7uG0Jh8X5NPgqNKxM2EwzZcNh8mLXnoTlqwFaNL2TD5lDYrBCpRrDJIssiVQCkaskAaEk5RLxVuAmhAXabjYzkRCK1ZeyvbZrETc/SML0WVLdcp+qfIy88uPVslZAQgNwhjxv1SXqOT0ikOzVyZ76IWVuGWVdJ9WdvkPbtf8emKpuMe2trIUGQEJ1N44RdFct+R/aNj+NISyS09mGCmxslA1LdqFWnkMBLiOO0NH54fd0623edWzq+awkZRMqOjUPeWrK66IUAqdcifrVUd7v96Dd4TSe4u5M6aTaWDvYxzwEWFqd/vG51v5UEcXFJs6LHON2VBiqIBijj2IWYyrJKQoEg+8jBMHQuyakmrHQ+re3PRZ59pBp1hIJ2bEYteiQRkr9G0yAQsWE3TNAgU6umgqyGeZZHKjHQSdQTom8kRS8EBUt2kJyWQnVlNZkpKWw/UITdH8JnOfHo0Qv4Rla0IW4eVNhSmzfK+2RGnyP/fE8h4/qPbo8iE12UNMjjSIvdHk2FaaTiTnZgT8omdfKF6A6N8Kq7micIGvP8cdML0bkcnz3YcBqEVt3dPBnQ0bp+om7CQpxIS+OHN65bdTS+e3fmdev443tWYtMOatL9XHT7scsSAAAgAElEQVRWLdVdR2omNeXRxlgdbjCBcOOpTq9en2y/FXEmEh1uzEjORYtEhwuzjGMJ3UoPRh912FjpZGjfbDyOSjb6euBXDka5d4GCYCjaVPEkVRFIOAhAIBIdJthwamRYFdCkQV5FmpGCztEGdXI3AOr2byVt8CSqK6vJTkvhq/0HCdeWUxzsTr+jh3vdpWGkagR3mdgGN28i2Q07A3P68vHOdTxw9Y9b7NYuxOmQLuudwPHdxFob91YSpIhzQePswaiTJ8MS4nTE43FUup+Lzur4uqvp7fPzMh73W9E6VbEDAFtKNzQz2iBX2rGLnYf2FYGmURT28M3zoxdU1nt74dGD9HMUAxA62iBPyDiEZkTHHfeHow1y051AWuRYYjelFGVmNMN6Pc3hQXMlU7vnS1LS0wHITElC1zSs2jJ2HjesuJGnES43oarlxvawHvkcrDrE3ooDp1coQrRAGuSdUH2CoMYkQYo4F0ldF+1F6pYQnY/st52LVb4diOYU0CO1KM1ANdpWRXsOYCV3w+byMCq7iojS2Og7jwL3fgyi3ci9NdFxxpOyixumq79Dbro9JIXKGt6vtbyEVYQ0PblpIMndqNu3mcTkZAxDx2YYZKQkQV0ZO2ua5pux5eqgQ3h7y3lohnXPB+CDHWtOp0iEaJE0yDsBw9BINPwkazUkGn78puekCYJamq618WyFiFctJcNyjHqckJJhnbqys3Fsay0x4OkmmjpVyrLk+CzOWFc7z7d1v+1q5RKvVNUuAHRXCnqkDmVLQh19XEFZFsV7DlCk5zAsfyCJoYPsCWYRUA4ucB1smEd1yUAsUycp41jDOxCJNuo1txNXuAqXEZ3nkUgFcCyhW0McSdkEDu1CQ5GangFAVmoK3poqDtcFqTWPNYc0h4atm45vRxhCzXsppSek0Cu9Oyu/WnXG5SNEPbmkGOcMQ8PtL2qW6Meb2AP3CRIEtTad391dukGKTiNiKgKuXNzfeBrTW45ZU8Hhd14ibfwMHEk9CYUkk3pXc7aOba0l0uyIxFCGoREq/ZrD/9+9cnwWp01ZVpc7z7dlv5XfP/FBKQtVd/Do3za0SC3KnghHRwUqKy0jGAhSmpTNNfndQJlsC3RDQ3G+veTodBq+svMJ+BNwu/1wdLQLS+kETRuG20ABWXotB8xkDkfKsWGQoic2iUVLykFFQniLvyYtM53yI0fITk1hKweI1JTxtfc8Chq14W29dSLFESJfg+2C5us2vEc+73y5grK6CjIT08962YmuR+6Qxzk3voaTCkTH9jz8l/twmF6q/S4qA4lU+11omt6m6dz4OnwdhDgTjkgtB383iwPP3UbxH+/Dt+UjSv/nAdxW7cknFuecs3lsq0+0Vn8c7agszW58HDraGAc5PovTY3oru+R5/mT7rfz+iQ9W+T40LRB9oWzokVqwJTR8Xrx7PwD+1D4Mz45um6/83ejpKMejhwAIVudhhT2EwjpOR9PhzQJhOw5ntOdDhqoE4HC4gkxbGhrH9Yg4mtitYscGUtLSotOkJGEzDPS6wxRWNr24b6RoGBk6vo1hVLj5eWFkryEoFO9sWnHK5SJES+QOeZzTrXDDSaVepLKkxQQmNkMjweFHVxE0TSdn6qPodh2zrpry5X8iuH+zJD4RnY9h0P3HL6AbBpZpUrHiz9Suewcsqctd0akcE+PV8evg7DmYzO/egcMRwal5ieDAG3TIME7ihFQk1On3hfZwpseIxr+lLM1GwPTgMnwNr70yxGabmPs3YrdrKGVHt0Kgwijj2ONme7/eT1h3MmDkN/F4dxO0bOwKZnFl0raGJPzeI+ej2/yETROPPQjRgdMA8IdtJHuC1Gk2MqwKIqoH5WYVFzr7NQ8mKRvQqNm9kZ7jb8LhsBMKhcnLTONwZSl7akwqwwZp9mPb1T5AI7DaIrIV7MOazi43JZuBOX1584v3uOUb30fX5P6mODNSg+KcpduxpeU2ec+WltssgYlSFknaIcxVdxFa+W+EVt2NPcHA2vUH2Pff5Fz3UzwXjpPEJ6JTcTp0DKuS8LoHCXzwI8LrHiTjyhtJGTcNdKnLXVFbj4nxrPE6OHsOJueGu1E7niWw4ocEP7sHw7efJKMCmzz3Kk5Aszk6/b7QHs7kGFE/znn9bylz1V0kqCKszf/V8DpJOyT7ZhtEDmxET3CD5oJgNdB0DPKd2/dT4czhomGDcXh3szOYjYnBAGf0YooZchGo6oE7czehkIHdFsSwGw3T+8J2XLqPOj2Z1NBhyiIVKBQZemqzWDTDjpaYif/ANgDSMjMB6J6ZTiDgx/TVUFjddJsaqXr0WfIvQ1j+5hdgxvUfTXH1YVbv2XCGJSWENMjjnh8P2dOebji5NDwLxXFJrYJVhNc1HZszVDgPW/+bUP5SIpufJuva/2w+nRBxLMFWR2jtI03r9bpHSR93I349KcbRiVho8zExjvnx0O1Hv8GWlkvGVbcQ+de8Zsdu/CUkOPwxjlTEMyMhrdPvC+3hTI4RCQ5/899Sax/B1mtiw+vwup/LvnkSSlmYBzdiJCWjNBeEow3y+jHIw8EQvrJS/Ck9uSDLQlNhtvm7YWDSx3YIAO/hAaA0PJk7CIVsaJrC7T7WMPaGHOiaRdCTQkLgCFVHhz9LPz7Der3kbEKHdhIJR0jNiD733T0r+v+UwGE+O2xSc1z3dHt/HRWB8L+aN8hHnHcBSa5EFq79+xmUlBBRXfsyaiehedLInfkiaAaW4cBnOpslJlFWuMWxOTV7UsPf6LokNBFxyzA03Pii3Q1rQxiGE62VMWdRliR066JMU+F3dyfnJ39CN0OgTCzd0dDFsTMwTYUjpx/Zt7+Cwx4m+FULx26bq8t3PRYnpuk6fnd3sm9/paE7tR9Plz/P1x8jji8XgETDT7iyjkTDaLGsdBUhcoLfUvWvZd88MatsL8pfg+7JApwQqom+r7lAWWzbuhdNKTKHXIan9isAtgVy6es8gl03UaaB9/AAXKlF2By1hILR5orHHaGuJnon2xuONu5JTkSvtUi0SkjSE3BoLZ8PVGIO4eKtVB4pISU9GwC300FGchKR6lKszP4sKdX5QXcLQ4suQ0/UsPfU8W8LYx/oRE87Nj+bYePqQWN5a+My1u37kot7DW2HkhRdhdwhj2P1mUJL/3s6B566lpKXb0fVlbX4XU23tzg2pwrXNvxtyfUXEacasuK+PJ2D86+haMEtuP1FqFbGnFWa0cqcRFeh6sooefl2Djx1LaX/PR23v6hTDW2k6Tp1phtTc7RcxyOBLt/1WJycaSrqTDc1VlK0PnXxxni948sFaDjH7P/1ZA6/3PIxo7Vxzut/S9W/ln3zxCIHCgHQ7AqleSBUjdJdcPRZ649X78BCY+hV38dVvYU608GBUDqDnCWgwFfWByviwpO9BYBgKHrOd7mPXQjxhhwA2BOiY5JnR8rINtJbvTirJecAiqqvN+J0OXEnRC/SdEtPpfhIOcMTg2yrsnjzoEZ1ozvl9n46mh0Cn0dQqunMrxo0loyEVJ5e/gcsJTcJxOmTBnkcO6VMoc7U5uM1F8wmsvO1Dh9fV4hT1VpdD1sOHKMeO24c8sfwRhJPNDtxjjuXsih7Q27so5ofu3HnyjFbiLOkrceMlsY5d4x6jMi+pQ2v5ffUyZl71qGn5h3Nsu6GUCXKkYqyTACKdu7Gl9SDvnkpGMFSvgp0Q6HR31mCUlBXOgi7pwJHQrS3QigUvQDidocblhEyDcKmQbI7TK3mYgAhcozM1oNK6Q6Ad1f0me/0zOh45D2yo9MYtaVckqmxo0bx3A5484Bin1ehOTTs/XRCJSZqf9MLOA6bnWuHfYuvDu3izQ3vnXnBiS5LLvHFsVPJFKppOrXq2NicSrNhaga2goc6dHxdIU5Ha3VdC/nx2rqTMPY5NGWiNANvJJGgdFfv0s6FTOv1Iqailm4kjnkegzBoumRZF+Isa+sxo6Vxzr2mB9fg/8B24Z3ye6oNVNBLZP8GnEMmoPEJFi4IVqIS+wCKzfvqcNUdQr/4RjzVm9GArf48XFqInrYjBCrOI+JPJbXPJxztOY5l6UQiBm5XCBpyAWjUhZwkObxsVU6G6AE2NO5TfjxPGpormdC+QgKBAKnpGZSVFJOS4CE10cPWvQeY0rsnPd2w02ewtcpkUzVckglXn6ejH1R414RJ7G5Dsx1rmI/uM4y1ezfy9PJXuKjnEPpl9WqnkhXnMrlDHsdONVNo47E5q/wuqn32Dh9fV4jTcaK6HgxZVPg8lPuTqPB5pDEuzolM641FTEWV30W5P4lyXwLVPrscs4U4i07lmHH8OOfBkHXCcc9FU+GvPwMzjJ7XH00zwdLACmHZos/hL/7oa3QsBlx+HZ6K9QBsDeQy0FWKhqK2eAiGswZX6t4m8w2FbLicTccirwvZSTBq+cJUZGjQ3dl6s0bTNEjvRejgFiorKklOS6W+xd89M4N9pWX4gkE8BgxLMpnSHYal66wuU/zzsI59oI5ZZxHZ0nS+uqZzyyU34LY7uf+tX1ETqDvDEhRdkTTI49i5kE1YiLaQui5OhdQXIcSpkGNGxwlveg89NQ89OSX6RijaQFX2RLxBi51bv0ZpBj37n48tUMLhcCJlkSQGOYsJVPYg7EsnKfdLNK3phY9gyIbT0bRB7g05cehB1mshAHprLedZapDei0jNYaoP7MRms5GUEo3xvKPd1ncXH2r4qk2HEckWQ1Ph8zKLbQ4DW66Ob2MI5W062xR3EjPG3sj+imLu+euThCJhhDgVnfN2QhfRWqZQSdoizjXH13XD4aQm3Hw0ASFAjo1CiFPT+Jhh00wiquUs6+LMmCVfYR78F65v3Ixu1YAOmr8CDBemnsDbX9aQVrsPV58Ckuv+BUSzqwMMcBRRu2sshrMWV/qeZvMOBXWSk/yARn3mtpqAC4CURJ06n5OeVjHryG02bYP0aHdy7/bVWBd/k/SsLMoOl5Oc4CE9KZF/7drLhb17Ru+mH1WQAqUBWFKs6N1PRy+1CH5h4bqs6T3N/G7nc9uYKbz86ev89P8e5enrf0aSK+F0i1J0MXKHPM5JBlXRVTSu67akDKnr4oTk2CiEOBX1xwx7Wq4cM9qBUorAJy+juZIweo5AU9FxwY26YkjpSygc4c1Pi0kNHaHnmO/iLl8LRJ8fTze8JNYmEvZlkJS7Ca2FVOnBkA3DiGB3HmssVwddWAq+mebmiJZOVqAIu3aC7ZqSi+ZMJLLrMyorKkjPym7ott47N5uS8irKa2qaTKJrcEk6BCKKj70G9t46ga/DWC3cjB/VexjTx/6Adfs2ccuf72dbydenWoyii5IGuRBCCCGEEOK0hTe+jbm/ENfFP0ApHaf/YwCUozuk9OdvG33YSqMN1D79c9BUmIjS2erPZZCrmNqioRiOOlzpu1qcf32m9QTPsTwyuwMhdvkUl2U4KCYDmwrT31HZaoyapkPOQIK711NaVITd4SAjK5ptvVdOFjbD4Mvd+5pNl+aAQcmw9ohJ9Xk6mlMjsLb5MGgAl/Qp4O4rbqHMW8nNr97DL/6xgEM1R9pYiqKrkgZ5nDMMjUTDT7JWQ6Lh71Tj7Apxtsn+ILoaqfOiJYahEaktl3oh4kJ41yoCHyzA1nskWveh2IK7MNQRlDIIpY6i2hfm96t89DP3kdBjIDnmVgC2+nPxKwcDg0HC3kwS8za2eHccIBSMjkXu8ZgN771fe5jVFXB+go8aeyohzc4gs3l39yZyBmEFvXi3ryYcCpHXsycAdpuNXjmZbNq9n1C4+TPgw1LArsP75Tr2fhqhQybm7pYXkZ/bj8cn3c1Vg8bwt43LmPS7Gfxy6e+kYS5aJQ3yOGYYGm5/EYdfns7B+ddw+OXpuP1FcuIVXZLsD6KrkTovWlJfL4oW3CL1QsRceNdq/H9/BCPrfFyjbwbLwlG3Buw20JKxrBC/WV5DuKqMhKo9nH/RcFTED8B6by/cWoisg72wJxzBndZKC5dol3UAtyc6VN3ekI+NgRrKvYkYmqJftyB76UZuYB9peqD1gLP6gc2BteMDDhWXkJqeTkJSIgB987oRjphs2Xeg2WQuA4alwlfVFgcyDIx0Hd/nYSx/yxcQPA43P7jou/zqmvu4rN/F/LVwKZN+N4MXP16IaZktTiO6LmmQxzE3Pg7/5b6GsTMjlSUc/st9uPHFODIhOp7sD6KrkTovWiL1QsSL8O7P8b/zCEZWH9xX/ARLaeiRCmyBnSinA0U6G/f7eX29l0sTj6AZNi7oH81sH7IMNvrPI9/0ooXdJPdci3aCa0rhsIFS4PGECSuLN6qKSNZt5EWyqAs5GJZTzG4tFwudy7Qt0EJ3cgDN5kTrPpzAtg84vHcXpmnS6/zzAUhLSiQvI43Pt+wgFIk0m3ZQIiTbYVkp2C7QscKK4OdWi13X66UnpHLTxd/jl9+7l9G9h/HSJ//L7QvnUl7Xetd60fV0qgb5nj17uPHGG5kwYQI33ngje/fujXVI7Uq3wg0n3HqRyhJ01fwgIcS5TvYH0dVInRctkXoh4kH0zvjDGBm9cF8+C+voo92OurXRROi6n0Akhbnv1pCTaJCw52P6DB6E3RG90/25tw8+y8nAsiQ8WTtwuMtPskSNQNCBx+3j9aoiDoYDTEnJxakbbClNI8Vey9gBlezQe3BecC9j9O0YqpU70b0uRoWDqG1LOXjgIBnZWSSnRodAG9SzB95AkC93Ne/6btOhIBVKfIrNlo6jv0FwT4Tw5pMnCMxITOOWS25gxtgfsKl4O1P/+B9sKd550ulE19CpGuSPPPIIN998M8uWLePmm2/m4YcfjnVI7crS7Q1jZtazpeViaTJaneh6ZH8QXY3UedESqRci1sI7P4k2xjN7H70zHm1OaGYdDu8XmCl90DTF/3we4lCNxbcTDmAFvRRcOgyFhVLwftVgssMWfbQqkrqvb9Nyq2ttKHs5a31VTEzKIt8RvdteUpvIjvJMBqSX0q2fk4O2XEaEtzDDfJdJ5ipGWNtJajR4uJbaHT1nAL7VCynavoVQMEj/Cy9A0zQyU5PpmZPJZ5u2U+tr3uukjwdyXBrLShS+Hhr27ga+dWHCe6xm323JN/oUMGfCLEBxy5/vZ/GXK9o0nTi3dZoGeXl5OVu3bmXSpEkATJo0ia1bt1JRURHjyNqPHw/Z055uOPHa0nLJnvY0fjwxjkyIjif7g+hqpM6Llki9ELEU3rYC/zuPYuT0w33FHQ2NcZTCVfUPUBFUogvT0nj5E4PJ/dzUrn6DC0YPw5McraMfHbqIEjOZ0b4g6f1XoOsn7t1RGY7w9uFK/rLHR7rbYlpuDle605p8Z2tpMrsq07kg8xCOXql87hjBYU9vsm0+xlib+ZG5lO9Ya0hRddFwL/g2VtAHH/+W7Vu34UlIoHf/fgAM6dsbgKXrNmJZTRvamgZj0hUhS/FWiQb5GrYMHe9HYUI72/Zs+HlpuTw44Q76Z/fmocXPcMfrD/Pe5g/5bNd6/rHlQ3774Z/50e/nMPnFmXxnwW3c+Ie7eOidZ/j7v96nwlvdpmWIzqXTXE4tKSkhJycHw4hmWTQMg+zsbEpKSkhPT49xdO3DNBV+d3eyb38FXUWwNBt+PDJ2puiSZH8QXY3UedGS+nrR/c4/YYaCUi9Eh1BmmOBnrxJa+xq27hfiumwmVqM6Z3jX4/BvpizrChK1j/lwWzKjHeXY3/8dWT3zGHbZxdSW5VJcMohFnlzyVJhxPRdjs3ubL0spioNh1td6+aLGxy5/EIAfuJ1AmO/1TGHf3uMfONfYVJKKTVcMyymi0teHtaV5QB5JTovz9cP0Cuyml1XMeiOfLxIHwNBJhP71DtVvzuWg65d0HzSUmqpKOFzGiAF9WbNtJ598uZXLhl2I1ugB91QHjM2AD48o/lqi8f1hOvZNGr5PIlg14CzQ0fQTJ1lMciVw1xU/YuX21Szb9gmf7friWFlqOn2yetAn8zzsukGVv5ZPd61j8aYVaGgUnHcBk4aM5+r8b5LsSjzlbSnij6ZOlIkgjmzevJnZs2ezZMmShve+853vMH/+fC688MIYRiaEEEIIIcS5R1km3i0fUrHsd0Qq95NWkI/T4wObB1JHUZkyFm/ll3Qv/TMVyYNwJH5NwBfk5ad2Ure/mO59BzJo2E/wVQ3Ebzr4v2w47NC5L+M98myHG5ZjKcVOX5D11V7W1/g4FIoOPdbX7eSiJA8jkt10d9m4eEQxFdVZrFmb0mK8GooxvUtJdfpYvL0/h2qP9RxxaWGGa/vJCR6k0kjlI304h/ZuJ7RpKVgR7Knd0HSdsK8GFfITtidRbM/FPfhqvvmtG7DZm97H3Fat+LA4QjePzpReBpm7TLy7QjhzbKRf5caZ3bb7nqZlUlx1hIgZwW130i0lE13XsRo10ZRS7CsvYcO+LazauYG95cU4bQ6+NXgsN1w8gcsHjcJudJr7rOI4naZBXl5ezoQJE1izZg2GYWCaJqNHj+af//xnm++Ql5fXYVnts7pZWUkcOVLbLvOW5cf38o9fdlZW0lmZb3vW1+PFevsdT+I5sbMZTzzW13gr7/bQFdYR4rOuwqnV13jdVvEaF0hs9cs5W9paX0+2bkpZKH8tynsEe9kK7LX/wjJ1/KFcIno2ekImelp3dJcDKxzCLNpOaPN72DmCp3sKrlQAk7BnIJbpxRU8NjxYiZlISl4VAW+IV375Lywrj255N5KcPB4Mk4NZlSxzplKhXMxM+5CBxh5KgmG2+wJsqvOxpS6Az7IwNLggwc2IJDfDE1yk2o0m69CvbxXZmX6Wf9wHv6/lJ2/tuskV/UrQNcU/tveitNaNTY+WX8g06GGrYYi5A5fpo9Seyc5QGvvKfAQDXjwpmdiTUgkHA9Qd3End7g3okQA+ZwaOId+h59gbcCakHlvvoManZVAXVlyYAhdpkLvDhKDC3kvHMdDA1k1Ds53asISpKR6qqlseOUEpxYGqEtbu/ZLVuzdQ7a8l1Z3MlQMvYfzAMQztMeiU75yfzfoqTl2nuZSSkZFBfn4+7777Ltdccw3vvvsu+fn5p9RdXT9J95Ez1d7zl+XH7/LbY9kdvT6x3n7Hk3hO7FyPJ97Wrz10hXWE+FzPU40pHtcB4jcukNjOpuPjVUoR/uoDrMqDqEgQFQ6gArWUmD6CNZUofw0qUIMyw2g2BxgONBXAnRrE5tRwptsw7Dphr4lh10hxHCDsNVFehVGrY9ijDV3LVJCvoesJRLBxkIv4ZFMdhTuKiATq6J/i58qxJi5PhB79TcoOBXn+T6kEBi4gktKHHY46yuzVVOhJRLQ8HFYlWaG/snDvXoqCYcJH7wlm2A1GJnsYnOhiiMeJy2g9xdWBokSyMv1c+o2D7NmXyK49qVhW0++HLYNVe7MZ2+cw113QNJN5bcjBkTo3GumkJTrJdYQYTilm0MJXp1HlryLgcONNKMD8xijK+TEfry0ksvpNPOv/wsH1C/Em5GEldQN3Cio5j7yeY9EyurPbm8KWiMLRzSBPg8Q6i4Q1JnYUNofGBYYiDcACZUX/jwUowAB00HTA0Ag4ajGV1fCesoAwqIhChSHNzGSCbTwTXFfgdfgpD1ZyaEMZy9ev4T0+JSHBRaI7gQSnm2BuAC0JhuQNYNyA0WdQE0V76TR3yAF27drFnDlzqKmpITk5mXnz5tG3b99YhyWEEEIIIUSHUkpFx9tWCmWZKDMMZiT6f6VAA003jrboFEopFBZEAqhQDZhBUCZoNjBcYDijr60wKAtLaaA0sEIoK4wyI0fnoVCAZZmADoYD0/RimV4iwaPPhDdqXVhHp4iG2kqz41RaI5qObvNgWh4C3jqsSKjVr9rsTkzNhoYCBUozMHEAGhomOhFsNg2l2YnobizDhdJs1Pr8DUOZV1VXYVkKj8uFzW7Hk5iKw+3GZneg6zaCEYtwxMQXiGCz6TgMDZeh4zDAbjPQbWCz6ViaImJGE9ip+lxx9QvRov/R9GjiOPSjb9UXjXV0U1tH36kvL01r+L6uaeiGjs2w4bTbcdgc2HQDzdAw0NE0DV3vNPm8u5RO1SAXQgghhBBCCCHOFXKZRAghhBBCCCGEiAFpkAshhBBCCCGEEDEgDXIhhBBCCCGEECIGpEEuhBBCCCGEEELEgDTIhRBCCCGEEEKIGJAGuRBCCCGEEEIIEQPSIBdCCCGEEEIIIWJAGuRCCCGEEEIIIUQM2GIdQGe3Z88e5syZQ1VVFampqcybN4/evXuftfnPmzePZcuWUVRUxOLFixkwYMBJl3s2Y6qsrOSBBx5g//79OBwOevXqxeOPP056ejobN27k4YcfJhgM0r17d+bPn09GRgbACT87VXfccQcHDx5E13U8Hg8///nPyc/P77AyqPfb3/6WF154oWE7dNT6ny1tKRPTNHnyySf55JNP0DSN22+/nSlTpsQsnhdeeIH//d//JTs7G4ARI0bwyCOPtEs8re1rjXVU+bQllo4sm47w2GOPsXr1ahwOBx6Ph7lz5zJkyBAAysrKeOCBBygqKsLpdPLEE08wbNiwGEd8+tr7vBELp3uuiGfxsp1OVLYDBw5kwIAB6Hr0/spTTz3FwIEDOzS+8ePH43A4cDqdANx3331ceumlMd/uBw8e5M4772x4XVtbS11dHWvXrm015s4g3s7lcOI62ticOXNYtWoVaWlpAEycOJGf/OQn7RZXvbZsb7/fz4MPPsiWLVswDIPZs2dzxRVXtFtMJ6qfjZ1r53rRCiXOyLRp09Tbb7+tlFLq7bffVtOmTTur81+3bp0qLi5WV1xxhdq+fXublns2Y6qsrFSff/55w+tf//rX6sEHH1SmaaqrrrpKrVu3Timl1IIFC9ScOXOUUuqEn52Ompqahr/ff/99de211yqlOq4MlFJq8+bNavr06Q3boSPX/2xpS5ksWrRI3Xbbbco0TVVeXgfdrQoAABJWSURBVK4uvfRSdeDAgZjF8/zzz6tf//rX7bL847W2rzXWUeXTllg6smw6wsqVK1UoFGr4+8orr2z4bM6cOWrBggVKqWjZXH311cqyrJjEeTa093kjFk7nXBHv4mU7tVa2Sik1YMAAVVdXF5O46rV0nIrH7f7kk0+qxx57TCnVcsydRbydy5U6cR1tbPbs2eovf/lLu8XRmrZs7xdeeEHNnTtXKaXUnj171JgxYzp032pcPxs71871omXSZf0MlJeXs3XrViZNmgTApEmT2Lp1KxUVFWdtGSNHjiQ3N7fNyz3bMaWmpjJ69OiG18OHD6e4uJjNmzfjdDoZOXIkAFOnTmXp0qUAJ/zsdCQlJTX8XVdXh6ZpHVoGoVCIxx9/nEcffbThvY5c/7OhrWXy3nvvMWXKFHRdJz09nauuuqpdYu+IfedUtbSvHa+jyqctsZxrrrjiCux2OxA9zhw6dAjLsgBYunQpU6dOBaJl43A42LRpU8xiPRPxWPfPhtM5V8SzeNpOrZVtPIu37R4KhVi8eDHf//73YxbD2RBv5/J6nbGOHu8f//gHN954IwC9e/dm8ODBfPzxxx2y7HOlforTJw3yM1BSUkJOTg6GYQBgGAbZ2dmUlJTEbLntGZNlWbz22muMHz+ekpIS8vLyGj5LT0/HsiyqqqpO+Nnpmjt3LpdffjnPPvss8+bN69Ay+K//+i++973v0aNHj4b3Onr9z1Rby+T42HNzczl06FDM4gFYsmQJkydP5rbbbqOwsPCsx3IqOqp82iqeyuZsWrhwIZdffjm6rlNZWYlSqknXx1iX+5mI1XmjI7X1XBHP4nU7NS7betOmTeOaa67hN7/5DaFQKCZx3XfffUyePJlHH32UmpqauNvuK1euJCcnhwsvvLDhveNj7gzi7VzekpbqaGOvvvoqkydP5o477mDXrl0dEhOcfHsXFxfTvXv3htcdWWYt1c/GztVzvThGGuSizZ544gk8Hg8//OEPO3zZv/jFL/jwww+55557eOqppzpsuYWFhWzevJmbb765w5Ypjpk6dSorVqxg8eLFTJ8+nTvuuIPKyspYhxUXOlvZXHfddYwePbrFf6ZpNnxvyZIlLF68uEmPFNG5xPJcca47vmw//PBD3nrrLRYuXMjXX3/NggULOjymhQsX8s477/C3v/0NpRSPP/54h8dwMn/729+a3H3sDDF3Vifa/++55x7ef/99Fi9ezLe+9S1mzJjR5PjfXuJ9ex9fPxvrbOd6cXqkQX4GcnNzKS0tbTiYmKbJ4cOH272r6YmW214xzZs3j3379vHcc8+h6zq5ublNuiNVVFSg6zqpqakn/OxMXXvttaxZs4Zu3bp1SBmsW7eOXbt2ceWVVzJ+/HgOHTrE9OnT2bdvX0zW/3S1tUyOj72kpIRu3brFLJ6srKyGbsxjx44lNzeXnTt3nvV42qqjyqct4q1sTmbRokWsWbOmxX/1d3vef/99nn32WV555RUyMzMBGpL/NO6SGctyP1OxOm90lFM5V8SzeNxOx5dtfZwAiYmJTJkyhQ0bNnR4XPUxOBwObr75ZjZs2BBX2720tJR169YxefLkE8bcGcTbufx4LdXRxnJychrev/baa/H5fB1yF7ot2zsvL4+ioqKG1x1VZi3Vz8Y627lenB5pkJ+BjIwM8vPzeffddwF49913yc/Pb5ZVsiOX2x4xPfPMM2zevJkFCxbgcDgAGDx4MIFAgPXr1wPw+uuvM3HixJN+dqq8Xm+TrlgrV64kJSWlw8rg9ttv59NPP2XlypWsXLmSbt268corrzBjxowOWf+zpa1lMnHiRN58800sy6KiooLly5czYcKEmMVTWlra8Pe2bdsoKiqiT58+Zz2etuqo8mmLeCubM/XBBx/wq1/9ildeeaXJ4yEQLffXX38dgPXr1xMIBBg8eHAswjxjsTpvdIRTPVfEs3jbTi2VbXV1NYFAAIBIJMKyZcvIz8/v0Lh8Ph+1tbUAKKV47733yM/Pj6vtvmjRIsaNG9dwca+1mDuDeDuXN9ZSHT1e4/PWJ598gq7r5OTktGtcbd3eEydO5I033gBg7969bNq0qUMy7x9fP493rp3rRcs0pZSKdRCd2a5du5gzZw41NTUkJyczb948+vbte9bm/+STT/LPf/6TsrIy0tLSSE1NZcmSJSdc7tmMaefOnUyaNInevXvjcrkA6NGjBwsWLGDDhg088sgjTYY0qb+rdaLPTkVZWRl33HEHfr8fXddJSUlh9uzZXHjhhR1WBo2NHz+el156iQEDBnTI+p9NrZXJzJkzueuuuxgyZAimafL444/z2WefATBz5syGJCexiGf27Nls2bIFXdex2+3cddddjBs3rl3iaW1fi0X5tCWWjiybjvCNb3wDu93e5Ifln/70J9LS0jhy5Aj3338/xcXFOJ1OHnvsMUaMGBHDaM9Me583YuF0zxXxLF62U2tlO2PGDB5++GE0TSMSiVBQUMDPfvYzEhISOiy2AwcO8NOf/hTTNLEsi/PPP5+HHnqI7OzsuNnuEyZMYO7cuVx22WUnjbkziLdzOZx4/7/mmmt4+eWXycnJ4ZZbbqG8vBxN00hMTOSBBx5g+PDh7RYXnHh7N47N5/MxZ84ctm3bhq7r3H///Vx11VXtGhs0r5/AOX2uFy2TBrkQQgghhBBCCBED0mVdCCGEEEIIIYSIAWmQCyGEEEIIIYQQMSANciGEEEIIIYQQIgakQS6EEEIIIYQQQsSANMiFEEIIIYQQQogYkAa5EKJTeuGFF7jvvvtiHYYQTaxZs6bJ8DVCCCGEECciDXLBkiVLmDJlCsOHD+eSSy5hypQpLFy4EKUUc+bMYfDgwRQUFDBq1ChuvfVWdu3aFeuQRScwfvx4Vq1axVtvvcVNN93U4nemTZvGkCFDKCgoYMSIEVx//fW8/PLLhEKhDoszFApx1113MX78eAYOHMiaNWuafK6UYv78+YwePZrRo0czf/58ZLTIc9v48eMZOnQoBQUFjBw5kqlTp/Laa69hWdZpzWvVqlVt/v6JjrmtXYQaOHAg+/btO+F3RNfUuC6PGTOGOXPm4PV6mTZtGm+++WaT7x5/Manxd9asWcPAgQN59NFHm0xz00038dZbbwHw1ltvkZ+fT0FBAQUFBYwfP54HH3yQPXv2tO9Kii7rRPW7/rdF/b9Zs2bFOlwhWiUN8i7uj3/8I7/4xS+YPn06n376KatWreKxxx5jw4YNhMNhAKZPn05hYSEff/wxOTk5zJ07N8ZRi3PJww8/TGFhIZ9++imzZ89myZIlzJw5s0MbvSNGjOCpp54iKyur2WdvvPEGy5cv5+9//zvvvPMOH3zwAa+//nqHxSZi46WXXqKwsJAPPviAmTNn8vvf/77Djn31x9yPPvqI9PR0HnzwwQ5Zrjg31dflRYsWsXnzZl588cXTmo/H4+Hvf/87Bw8ebPU7w4cPp7CwkPXr1/OnP/0Jp9PJ9ddfz44dO043fCFOqLX6Xf/bov7fSy+9FONIhWidNMi7sNraWp5//nkeeeQRJk6cSGJiIpqmccEFF/Cb3/wGh8PR5Psul4tvf/vbfPXVVzGKWJzLPB4Po0eP5sUXX2Tjxo18+OGHpzT9ihUr+O53v8vIkSOZNm1ak54cW7Zs4dprr6WgoIC77rqLu+++m2effRYAh8PBLbfcwsiRI9H15ofEt99+m9tuu41u3bqRk5PDrbfeyqJFi85oXUXnkZSUxJVXXslzzz3HokWL2LFjB6FQiHnz5nH55ZczZswYHn74YQKBQLNp77//foqLi5k1axYFBQX8/ve/B+Cuu+5i7NixXHTRRfzbv/0bO3fubHHZbrebyZMnt/q5EKciJyeHSy+99LTrU1JSEtdffz0LFiw46XcNw6Bnz548+uijjBo1it/+9rentUwh2upM67cQsSQN8i6ssLCQUCjElVde2abv+3w+3n33XXr27NnOkYmuLC8vj8GDB7N+/fo2T7Nnzx7uvfdefvazn7F69Wouu+wyZs2aRSgUIhQK8e///u9cd911rF27lkmTJrF8+fI2z3vnzp0MGjSo4fWgQYPkhN8FDR06lG7durF+/Xqefvpp9uzZw9tvv80///lPDh8+3GIjZf78+eTl5TXcwZk5cyYAl112GcuWLWP16tVccMEFrXYx93q9LF68mPz8/HZdN9E1lJSU8PHHH59RfZo1axbLli1j9+7dbZ7m6quvPqXjuRCn42zUbyFixRbrAETsVFZWkpaWhs12rBpMnTqVr7/+mlAoxCuvvAJEu7UvXLiQuro68vLy+N3vfherkEUXkZ2dTXV1dZu//9577zFu3DjGjh0LRLv8/vnPf6awsBBd14lEIvy///f/0DSNb33rWwwZMqTN8/b5fCQmJja8TkpKwufzoZRC07S2r5To9Orr5f/93//xzjvvkJqaCsCPf/xj7r33Xu699942zeeGG25o+PunP/0pF198MbW1tSQlJQHHjrlOp5MhQ4bw61//uuH7S5cuPeXeI6Jru/POOzEMg6SkJMaNG8esWbOYOXMmTz75JPPmzWv4nmmaDXWwNVlZWUydOpXnn3+e5557rk3LP9XjuRCnoq31+4c//CF33313DCMVonXSIO/CUlNTqaysJBKJNDTK65+NveyyyxoSGN12223cc889FBcXM2PGDPbs2dPkjqEQZ1tpaSkFBQVt/v7hw4fJy8treK3rOrm5uZSWlmIYBjk5OU0az7m5uW2et8fjwev1Nryuq6vD4/FIY7wLKi0txTRN/H4/119/fcP7Sqk2J3wzTZNnn32WpUuXUlFR0fCYRGVlZUNjqP6Y25KJEyfy9NNPN3lv4MCBp7M6ootYsGABY8aMafb+Qw89xJQpUxper1mzhvvvv/+k85s5cyZXX311mx9fKy0tJSUlpe0BC3EK2lq/hYhn0mW9CysoKMDhcLBixYo2fT8vL4+5c+fyi1/8osXnJYU4G0pKStiyZQsjR45s8zTZ2dkUFxc3vFZKUVJSQk5ODllZWZSWljZJEldSUtLmeffv37/JD8+vvvqK/v37t3l6cW748ssvKS0t5aqrrsLlcrFkyRLWr1/P+vXr+eKLLygsLGzTfBYvXsyKFSt49dVX+eKLL1i5ciWAZO4XnUZaWho/+tGP2nyHfPny5ad0PBdCiK5GGuRdWHJyMnfeeSePPfYYS5cupa6uDsuy2LZtG36/v8Vpxo4dS3Z2Nm+88UYHRys6M6UUwWCwyb/j+f1+1q5dyx133MHQoUMZN25cm+f/7W9/m48++ojVq1cTDof54x//iMPhoKCggOHDh2MYBv/zP/9DJBJh+fLlbNq0qcn0oVCoIaZwOEwwGGxoIF1zzTW8+uqrlJaWUlpayquvvsp11113BqUhOpO6ujo++OAD/vM//5Pvfe97DBo0iClTpvDLX/6S8vJyIHoH8JNPPmlx+szMTA4cONDw2uv14nA4SEtLw+/388wzz3TIeghxNt16660UFha2+iy5aZocOHCAJ554grVr13LnnXd2cIRCCNF5SJf1Lm7mzJnk5OTwhz/8gdmzZ+N2uznvvPO47777KCgoaDGb9IwZM/jVr37FTTfd1CwTuxAtKSwsZOjQoU3e27JlCwCPP/44v/zlLwHo1asXEyZM4Lbbbmsx43lr+vbty/z583niiScoLS0lPz+fl156qaF+vvDCCzz00EM888wzXHrppVx++eVN6u7EiRMpKioCos+fQzRre48ePZg6dSoHDhxg8uTJQPT536lTp55mSYjOYtasWRiGga7r9OvXj1tvvbVhu99///0sWLCAH/zgB1RWVpKTk8NNN93EpZde2mw+t99+O08++STz58/nJz/5CVOnTuXTTz/l0ksvJTU1lf/4j//gtdde6+jVE+KMJCYmMmPGjGaPT2zcuJGCggKUUqSlpTFq1Cj++te/cv7558coUtFVNf5tAdCnTx/eeuutGEYkROs0Jf3khBBdzJQpU5g6dSrf//73Yx2KEEIIIYTowqTLuhDinLd27VqOHDlCJBJh0aJFbN++vcW7mUIIIYQQQnQk6bIuhIhbM2bM4Isvvmj2/o9//GNmzZrV5vns2bOHu+++G7/fT48ePXj++efJzs4+m6EKIYQQQghxyqTLuhBCCCGEEEIIEQPSZV0IIYQQQgghhIgBaZALIYQQQgghhBAxIA1yIYQQQgghhBAiBqRBLoQQQgghhBBCxIA0yIUQQgghhBBCiBiQBrkQQgghhBBCCBED/z8CH2siYnw/jwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 984.425x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeCzbkegoxv3",
        "colab_type": "text"
      },
      "source": [
        "## Conditioning the data set\n",
        "\n",
        "Now we extract just the feature variables we need to perform the classification.  The predictor variables are the five wireline values and two geologic constraining variables. We also get a vector of the facies labels that correspond to each feature vector.\n",
        "\n",
        "现在，我们仅提取执行分类所需的特征变量。 预测变量是五个有线值和两个地质约束变量。 我们还获得了与每个特征向量相对应的相标签向量。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "fdx8XRKqoxv3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "d0371afe-7e9c-41df-a344-69cd8ba1029a"
      },
      "source": [
        "correct_facies_labels = training_data['Facies'].values\n",
        "\n",
        "feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
        "feature_vectors.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.249445</td>\n",
              "      <td>0.644637</td>\n",
              "      <td>3.754963</td>\n",
              "      <td>13.118929</td>\n",
              "      <td>3.805693</td>\n",
              "      <td>1.523895</td>\n",
              "      <td>0.523057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>31.610849</td>\n",
              "      <td>0.241922</td>\n",
              "      <td>5.045916</td>\n",
              "      <td>7.389665</td>\n",
              "      <td>0.894118</td>\n",
              "      <td>0.499518</td>\n",
              "      <td>0.287499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>13.250000</td>\n",
              "      <td>-0.025949</td>\n",
              "      <td>-21.832000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>46.081500</td>\n",
              "      <td>0.497000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>8.165000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.276500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>65.541000</td>\n",
              "      <td>0.627000</td>\n",
              "      <td>3.581000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.529000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>80.714000</td>\n",
              "      <td>0.812323</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>16.144000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.771500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>361.150000</td>\n",
              "      <td>1.480000</td>\n",
              "      <td>18.500000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>8.094000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                GR    ILD_log10  ...         NM_M       RELPOS\n",
              "count  2783.000000  2783.000000  ...  2783.000000  2783.000000\n",
              "mean     66.249445     0.644637  ...     1.523895     0.523057\n",
              "std      31.610849     0.241922  ...     0.499518     0.287499\n",
              "min      13.250000    -0.025949  ...     1.000000     0.010000\n",
              "25%      46.081500     0.497000  ...     1.000000     0.276500\n",
              "50%      65.541000     0.627000  ...     2.000000     0.529000\n",
              "75%      80.714000     0.812323  ...     2.000000     0.771500\n",
              "max     361.150000     1.480000  ...     2.000000     1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEDwUBRLoxv6",
        "colab_type": "text"
      },
      "source": [
        "Scikit includes a [preprocessing](http://scikit-learn.org/stable/modules/preprocessing.html) module that can 'standardize' the data (giving each variable zero mean and unit variance, also called *whitening*).  Many machine learning algorithms assume features will be standard normally distributed data (ie: Gaussian with zero mean and unit variance).  The factors used to standardize the training set must be applied to any subsequent feature set that will be input to the classifier.  The `StandardScalar` class can be fit to the training set, and later used to standardize any training data.\n",
        "\n",
        "Scikit包含一个预处理模块，可以“标准化”数据（赋予每个变量零均值和单位方差，也称为白化）。 许多机器学习算法都假定特征将是标准的正态分布数据（即：均值和单位方差为零的高斯）。 用于标准化训练集的因素必须应用于将输入到分类器的任何后续功能集中。 StandardScalar类可以适合于训练集，并在以后用于标准化任何训练数据。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bz6rBg4ooxv6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
        "scaled_features = scaler.transform(feature_vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IiC-N8VUoxv9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "47a73593-bbb6-4baa-99cc-d3cd37eba157"
      },
      "source": [
        "feature_vectors"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>77.450</td>\n",
              "      <td>0.664</td>\n",
              "      <td>9.900</td>\n",
              "      <td>11.915</td>\n",
              "      <td>4.600</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>78.260</td>\n",
              "      <td>0.661</td>\n",
              "      <td>14.200</td>\n",
              "      <td>12.565</td>\n",
              "      <td>4.100</td>\n",
              "      <td>1</td>\n",
              "      <td>0.979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>79.050</td>\n",
              "      <td>0.658</td>\n",
              "      <td>14.800</td>\n",
              "      <td>13.050</td>\n",
              "      <td>3.600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>86.100</td>\n",
              "      <td>0.655</td>\n",
              "      <td>13.900</td>\n",
              "      <td>13.115</td>\n",
              "      <td>3.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74.580</td>\n",
              "      <td>0.647</td>\n",
              "      <td>13.500</td>\n",
              "      <td>13.300</td>\n",
              "      <td>3.400</td>\n",
              "      <td>1</td>\n",
              "      <td>0.915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3227</th>\n",
              "      <td>46.719</td>\n",
              "      <td>0.947</td>\n",
              "      <td>1.828</td>\n",
              "      <td>7.254</td>\n",
              "      <td>3.617</td>\n",
              "      <td>2</td>\n",
              "      <td>0.685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3228</th>\n",
              "      <td>44.563</td>\n",
              "      <td>0.953</td>\n",
              "      <td>2.241</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.344</td>\n",
              "      <td>2</td>\n",
              "      <td>0.677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3229</th>\n",
              "      <td>49.719</td>\n",
              "      <td>0.964</td>\n",
              "      <td>2.925</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.190</td>\n",
              "      <td>2</td>\n",
              "      <td>0.669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3230</th>\n",
              "      <td>51.469</td>\n",
              "      <td>0.965</td>\n",
              "      <td>3.083</td>\n",
              "      <td>7.708</td>\n",
              "      <td>3.152</td>\n",
              "      <td>2</td>\n",
              "      <td>0.661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3231</th>\n",
              "      <td>50.031</td>\n",
              "      <td>0.970</td>\n",
              "      <td>2.609</td>\n",
              "      <td>6.668</td>\n",
              "      <td>3.295</td>\n",
              "      <td>2</td>\n",
              "      <td>0.653</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2783 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          GR  ILD_log10  DeltaPHI   PHIND     PE  NM_M  RELPOS\n",
              "0     77.450      0.664     9.900  11.915  4.600     1   1.000\n",
              "1     78.260      0.661    14.200  12.565  4.100     1   0.979\n",
              "2     79.050      0.658    14.800  13.050  3.600     1   0.957\n",
              "3     86.100      0.655    13.900  13.115  3.500     1   0.936\n",
              "4     74.580      0.647    13.500  13.300  3.400     1   0.915\n",
              "...      ...        ...       ...     ...    ...   ...     ...\n",
              "3227  46.719      0.947     1.828   7.254  3.617     2   0.685\n",
              "3228  44.563      0.953     2.241   8.013  3.344     2   0.677\n",
              "3229  49.719      0.964     2.925   8.013  3.190     2   0.669\n",
              "3230  51.469      0.965     3.083   7.708  3.152     2   0.661\n",
              "3231  50.031      0.970     2.609   6.668  3.295     2   0.653\n",
              "\n",
              "[2783 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "knTxuu5aoxv_",
        "colab_type": "text"
      },
      "source": [
        "Scikit also includes a handy function to randomly split the training data into training and test sets.  The test set contains a small subset of feature vectors that are not used to train the network.  Because we know the true facies labels for these examples, we can compare the results of the classifier to the actual facies and determine the accuracy of the model.  Let's use 20% of the data for the test set.\n",
        "\n",
        "Scikit还包括方便的功能，可将训练数据随机分为训练和测试集。 测试集包含一小部分特征向量，这些特征向量不用于训练网络。 因为我们知道这些示例的真实相标签，所以我们可以将分类器的结果与实际相进行比较，并确定模型的准确性。 让我们将20％的数据用于测试集。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77lBW0Vbbch8",
        "colab_type": "text"
      },
      "source": [
        "##导入我们的本地py模块"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjGb0UhWYPbu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/My Drive/Colab Notebooks')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTztjnmloxwA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "        scaled_features, correct_facies_labels, test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WZaSxbaoxwD",
        "colab_type": "text"
      },
      "source": [
        "##Training the Classification Tree\n",
        "使用决策树\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWRb1D0TTJpL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "48f9b54b-1e77-4f41-edbd-535516710eeb"
      },
      "source": [
        "from __future__ import division, print_function\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "import matplotlib.pyplot as plt\n",
        "import sys\n",
        "import os\n",
        "\n",
        "# Import helper functions\n",
        "from utils import train_test_split, standardize, accuracy_score\n",
        "from utils import mean_squared_error, calculate_variance, Plot\n",
        "from decision_tree_model import ClassificationTree\n",
        "\n",
        "def main():\n",
        "\n",
        "    print (\"-- Classification Tree --\")\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        scaled_features, correct_facies_labels, test_size=0.2)\n",
        "\n",
        "    clf = ClassificationTree()\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    print (\"Accuracy:\", accuracy)\n",
        "    print(f'准确率：{accuracy:.2f}')\n",
        "\n",
        "    Plot().plot_in_2d(X_test, y_pred,\n",
        "        title=\"Decision Tree\",\n",
        "        accuracy=accuracy)\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-- Classification Tree --\n",
            "Accuracy: 0.60431654676259\n",
            "准确率：0.60\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_r1dwF2oxwO",
        "colab_type": "text"
      },
      "source": [
        "We need some metrics to evaluate how good our classifier is doing.  A [confusion matrix](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) is a table that can be used to describe the performance of a classification model.  [Scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) allows us to easily create a confusion matrix by supplying the actual and predicted facies labels.\n",
        "\n",
        "The confusion matrix is simply a 2D array.  The entries of confusion matrix `C[i][j]` are equal to the number of observations predicted to have facies `j`, but are known to have facies `i`.  \n",
        "\n",
        "To simplify reading the confusion matrix, a function has been written to display the matrix along with facies labels and various error metrics.  See the file `classification_utilities.py` in this repo for the `display_cm()` function.\n",
        "\n",
        "我们需要一些指标来评估分类器的效果。 混淆矩阵是可用于描述分类模型性能的表。 Scikit-learn通过提供实际和预测的相貌标签，使我们能够轻松创建混淆矩阵。\n",
        "\n",
        "混淆矩阵只是2D数组。 混淆矩阵C [i] [j]的条目等于预测具有相j的观测次数，但已知具有相i。\n",
        "\n",
        "为了简化读取混淆矩阵，已编写了一个函数来显示矩阵以及相标和各种错误度量。 有关display_cm（）函数，请参见此存储库中的文件category_utilities.py。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BT0VKigGmQ9y",
        "colab_type": "text"
      },
      "source": [
        "这里需要导入一个py模块具体方法如下："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSgtFGommg4b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "9c45f1ba-7408-4529-d772-b5d01317e4a0"
      },
      "source": [
        "#导入py模块\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "#加入路径\n",
        "import sys\n",
        "sys.path.append('/content/drive/My Drive/Colab Notebooks/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4HrWWJjoxwP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "03a792eb-5c11-4065-d92c-f5c619da6596"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from classification_utilities import display_cm, display_adj_cm\n",
        "conf = confusion_matrix(y_test, predicted_labels)\n",
        "display_cm(conf, facies_labels, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-8c8acd448c39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mclassification_utilities\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdisplay_cm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay_adj_cm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mconf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdisplay_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacies_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhide_zeros\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'predicted_labels' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSp7tkdMoxwT",
        "colab_type": "text"
      },
      "source": [
        "The rows of the confusion matrix correspond to the actual facies labels.  The columns correspond to the labels assigned by the classifier.  For example, consider the first row. For the feature vectors in the test set that actually have label `SS`, 23 were correctly indentified as `SS`, 21 were classified as `CSiS` and 2 were classified as `FSiS`.\n",
        "\n",
        "混淆矩阵的行对应于实际的相图。列对应于分类器分配的标签。例如，考虑第一行。对于实际存在标签SS的测试集中的特征向量，正确识别为SS的有23个，识别为CSiS的有21个，识别为FSiS的有2个。\n",
        "\n",
        "The entries along the diagonal are the facies that have been correctly classified.  Below we define two functions that will give an overall value for how the algorithm is performing.  The accuracy is defined as the number of correct classifications divided by the total number of classifications.\n",
        "\n",
        "沿对角线的条目是已正确分类的相。 在下面，我们定义了两个函数，它们将为算法的执行提供总体价值。 准确度定义为正确分类的数量除以分类的总数。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2-bI3fwoxwT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy(conf):\n",
        "    total_correct = 0.\n",
        "    nb_classes = conf.shape[0]\n",
        "    for i in np.arange(0,nb_classes):\n",
        "        total_correct += conf[i][i]\n",
        "    acc = total_correct/sum(sum(conf))\n",
        "    return acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxuaqShqoxwW",
        "colab_type": "text"
      },
      "source": [
        "As noted above, the boundaries between the facies classes are not all sharp, and some of them blend into one another.  The error within these 'adjacent facies' can also be calculated.  We define an array to represent the facies adjacent to each other.  For facies label `i`, `adjacent_facies[i]` is an array of the adjacent facies labels.\n",
        "\n",
        "如上所述，各相类型之间的界限并不都很明显，其中一些相互融合。这些“相邻相”内的误差也可以计算出来。我们定义了一个数组来表示彼此相邻的相。对于相标志i，相邻相[i]是相邻相标志的数组。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8F9TJVutoxwX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
        "\n",
        "def accuracy_adjacent(conf, adjacent_facies):\n",
        "    nb_classes = conf.shape[0]\n",
        "    total_correct = 0.\n",
        "    for i in np.arange(0,nb_classes):\n",
        "        total_correct += conf[i][i]\n",
        "        for j in adjacent_facies[i]:\n",
        "            total_correct += conf[i][j]\n",
        "    return total_correct / sum(sum(conf))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bL1KemCeoxwa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf))\n",
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf, adjacent_facies))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDsp6A_Boxwd",
        "colab_type": "text"
      },
      "source": [
        "## Model parameter selection\n",
        "\n",
        "The classifier so far has been built with the default parameters.  However, we may be able to get improved classification results with optimal parameter choices.\n",
        "\n",
        "到目前为止，分类器是使用默认参数构建的。然而，我们可能会得到改进的分类结果与最佳参数的选择。\n",
        "\n",
        "We will consider two parameters.  The parameter `C` is a regularization factor, and tells the classifier how much we want to avoid misclassifying training examples.  A large value of C will try to correctly classify more examples from the training set, but if `C` is too large  it may 'overfit' the data and fail to generalize when classifying new data. If `C` is too small then the model will not be good at fitting outliers and will have a large error on the training set.\n",
        "\n",
        "我们将考虑两个参数。 参数“ C”是一个正则化因子，它告诉分类器我们要避免对训练样本进行错误分类的程度。 较大的C值会尝试从训练集中正确分类更多示例，但是如果C值太大，则可能会“拟合”数据，并且在分类新数据时无法归纳。 如果C太小，则模型将无法很好地拟合离群值，并且训练集上的误差会很大。\n",
        "\n",
        "The SVM learning algorithm uses a kernel function to compute the distance between feature vectors.  Many kernel functions exist, but in this case we are using the radial basis function `rbf` kernel (the default).  The `gamma` parameter describes the size of the radial basis functions, which is how far away two vectors in the feature space need to be to be considered close.\n",
        "\n",
        "支持向量机学习算法利用核函数计算特征向量之间的距离。存在许多内核函数，但在本例中，我们使用径向基函数“rbf”内核(默认值)。参数描述径向基函数的大小，也就是特征空间中两个向量需要被认为是接近的距离。\n",
        "\n",
        "We will train a series of classifiers with different values for `C` and `gamma`.  Two nested loops are used to train a classifier for every possible combination of values in the ranges specified.  The classification accuracy is recorded for each combination of parameter values.  The results are shown in a series of plots, so the parameter values that give the best classification accuracy on the test set can be selected.\n",
        "\n",
        "我们将针对“ C”和“ gamma”训练一系列具有不同值的分类器。 两个嵌套循环用于为指定范围内值的每种可能组合训练分类器。 记录每个参数值组合的分类精度。 结果显示在一系列图中，因此可以选择在测试集上提供最佳分类精度的参数值。\n",
        "\n",
        "This process is also known as 'cross validation'.  Often a separate 'cross validation' dataset will be created in addition to the training and test sets to do model selection.  For this tutorial we will just use the test set to choose model parameters.\n",
        "\n",
        "这个过程也被称为“交叉验证”。通常，除了用于进行模型选择的培训和测试集之外，还会创建一个单独的“交叉验证”数据集。在本教程中，我们将使用测试集来选择模型参数。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Cg8LHnpoxwd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model selection takes a few minutes, change this variable\n",
        "#to true to run the parameter loop\n",
        "do_model_selection = True\n",
        "\n",
        "if do_model_selection:\n",
        "    C_range = np.array([.01, 1, 5, 10, 20, 50, 100, 1000, 5000, 10000])\n",
        "    gamma_range = np.array([0.0001, 0.001, 0.01, 0.1, 1, 10])\n",
        "    \n",
        "    fig, axes = plt.subplots(3, 2, \n",
        "                        sharex='col', sharey='row',figsize=(10,10))\n",
        "    plot_number = 0\n",
        "    for outer_ind, gamma_value in enumerate(gamma_range):\n",
        "        row = int(plot_number / 2)\n",
        "        column = int(plot_number % 2)\n",
        "        cv_errors = np.zeros(C_range.shape)\n",
        "        train_errors = np.zeros(C_range.shape)\n",
        "        for index, c_value in enumerate(C_range):\n",
        "            \n",
        "            clf = svm.SVC(C=c_value, gamma=gamma_value)\n",
        "            clf.fit(X_train,y_train)\n",
        "            \n",
        "            train_conf = confusion_matrix(y_train, clf.predict(X_train))\n",
        "            cv_conf = confusion_matrix(y_test, clf.predict(X_test))\n",
        "        \n",
        "            cv_errors[index] = accuracy(cv_conf)\n",
        "            train_errors[index] = accuracy(train_conf)\n",
        "\n",
        "        ax = axes[row, column]\n",
        "        ax.set_title('Gamma = %g'%gamma_value)\n",
        "        ax.semilogx(C_range, cv_errors, label='CV error')\n",
        "        ax.semilogx(C_range, train_errors, label='Train error')\n",
        "        plot_number += 1\n",
        "        ax.set_ylim([0.2,1])\n",
        "        \n",
        "    ax.legend(bbox_to_anchor=(1.05, 0), loc='lower left', borderaxespad=0.)\n",
        "    fig.text(0.5, 0.03, 'C value', ha='center',\n",
        "             fontsize=14)\n",
        "             \n",
        "    fig.text(0.04, 0.5, 'Classification Accuracy', va='center', \n",
        "             rotation='vertical', fontsize=14)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bodJLJSuoxwg",
        "colab_type": "text"
      },
      "source": [
        "The best accuracy on the cross validation error curve was achieved for `gamma = 1`, and `C = 10`.  We can now create and train an optimized classifier based on these parameters:\n",
        "\n",
        "在gamma = 1和C = 10时，交叉验证误差曲线的精度最佳。现在，我们可以根据这些参数创建和训练一个优化的分类器"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_k0DgqJoxwh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = svm.SVC(C=10, gamma=1)        \n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "cv_conf = confusion_matrix(y_test, clf.predict(X_test))\n",
        "\n",
        "print('Optimized facies classification accuracy = %.2f' % accuracy(cv_conf))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxZOv_7boxwk",
        "colab_type": "text"
      },
      "source": [
        "[Precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall) are metrics that give more insight into how the classifier performs for individual facies.  Precision is the probability that given a classification result for a sample, the sample actually belongs to that class.  Recall is the probability that a sample will be correctly classified for a given class.\n",
        "\n",
        "Precision and recall can be computed easily using the confusion matrix.  The code to do so has been added to the `display_confusion_matrix()` function:\n",
        "\n",
        "精度和回忆度是能够更深入地了解分类器对单个相的执行情况的指标。精度是给定一个样本的分类结果，这个样本实际上属于这个类别的概率。召回率是样本将被正确分类为给定类的概率。使用混淆矩阵可以很容易地计算出精确度和查全率。这样做的代码已经添加到display confusion matrix()函数中"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4dKTWldoxwk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "display_cm(cv_conf, facies_labels, \n",
        "           display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w22nkAhIoxwn",
        "colab_type": "text"
      },
      "source": [
        "To interpret these results, consider facies `SS`.  In our test set, if a sample was labeled `SS` the probability the sample was correct is 0.8 (precision).  If we know a sample has facies `SS`, then the probability it will be correctly labeled by the classifier is 0.78 (recall).  It is desirable to have high values for both precision and recall, but often when an algorithm is tuned to increase one, the other decreases.  The [F1 score](https://en.wikipedia.org/wiki/Precision_and_recall#F-measure) combines both to give a single measure of relevancy of the classifier results.\n",
        "\n",
        "These results can help guide intuition for how to improve the classifier results.  For example, for a sample with facies `MS` or mudstone, it is only classified correctly 57% of the time (recall).  Perhaps this could be improved by introducing more training samples.  Sample quality could also play a role.  Facies `BS` or bafflestone has the best `F1` score and relatively few training examples.  But this data was handpicked from other wells to provide training examples to identify this facies.\n",
        "\n",
        "We can also consider the classification metrics when we consider misclassifying an adjacent facies as correct:\n",
        "\n",
        "为了解释这些结果，考虑相SS。在我们的测试集中，如果一个样品被标记为SS，样品的正确概率是0.8(精度)。如果我们知道一个样品有相SS，那么它被分类器正确标记的概率是0.78 (recall)。我们希望具有较高的精度和回忆度值，但是通常当一个算法调优为增加一个时，另一个会减少。F1得分结合两者给一个单一的衡量分类器结果的相关性。\n",
        "\n",
        "这些结果可以帮助指导如何改进分类器结果的直觉。 例如，对于具有相MS或泥岩相的样品，只有57％的时间正确分类（召回）。 也许可以通过引入更多的训练样本来改善这一点。 样品质量也可能起一定作用。 面相BS或Bafflestone具有最佳的F1分数，并且训练示例相对较少。 但是，这些数据是从其他井中挑选出来的，以提供训练实例来识别这种相。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKdxaGzIoxwn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "display_adj_cm(cv_conf, facies_labels, adjacent_facies, \n",
        "           display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tS5twC-3oxwp",
        "colab_type": "text"
      },
      "source": [
        "Considering adjacent facies, the `F1` scores for all facies types are above 0.9, except when classifying `SiSh` or marine siltstone and shale.  The classifier often misclassifies this facies (recall of 0.66), most often as wackestone. \n",
        "\n",
        "These results are comparable to those reported in Dubois et al. (2007)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fsJMjj5oxwq",
        "colab_type": "text"
      },
      "source": [
        "## Applying the classification model to the blind data\n",
        "\n",
        "We held a well back from the training, and stored it in a dataframe called `blind`:\n",
        "\n",
        "blind验证集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_q3K2toCoxwq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "blind"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y93bVHploxws",
        "colab_type": "text"
      },
      "source": [
        "The label vector is just the `Facies` column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88B-MECooxws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_blind = blind['Facies'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pucYEPJoxwv",
        "colab_type": "text"
      },
      "source": [
        "We can form the feature matrix by dropping some of the columns and making a new dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmTliVFzoxwv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "well_features = blind.drop(['Facies', 'Formation', 'Well Name', 'Depth'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwy99IqXoxwy",
        "colab_type": "text"
      },
      "source": [
        "Now we can transform this with the scaler we made before:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXhp3bkzoxwy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_blind = scaler.transform(well_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2CHYW_4oxw0",
        "colab_type": "text"
      },
      "source": [
        "Now it's a simple matter of making a prediction and storing it back in the dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cqqqSb4oxw1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = clf.predict(X_blind)\n",
        "blind['Prediction'] = y_pred"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCKmMWc6oxw3",
        "colab_type": "text"
      },
      "source": [
        "Let's see how we did with the confusion matrix:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jV72l6Xwoxw3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv_conf = confusion_matrix(y_blind, y_pred)\n",
        "\n",
        "print('Optimized facies classification accuracy = %.2f' % accuracy(cv_conf))\n",
        "print('Optimized adjacent facies classification accuracy = %.2f' % accuracy_adjacent(cv_conf, adjacent_facies))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DjQ7Didoxw8",
        "colab_type": "text"
      },
      "source": [
        "We managed 0.75 using the test data, but it was from the same wells as the training data. This more reasonable test does not perform as well..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXcBKVMcoxw9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "display_cm(cv_conf, facies_labels,\n",
        "           display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_WYYCYMoxxA",
        "colab_type": "text"
      },
      "source": [
        "...but does remarkably well on the adjacent facies predictions. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41HZ0-F1oxxA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "display_adj_cm(cv_conf, facies_labels, adjacent_facies,\n",
        "               display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ISZ6v7DoxxD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compare_facies_plot(logs, compadre, facies_colors):\n",
        "    #make sure logs are sorted by depth\n",
        "    logs = logs.sort_values(by='Depth')\n",
        "    cmap_facies = colors.ListedColormap(\n",
        "            facies_colors[0:len(facies_colors)], 'indexed')\n",
        "    \n",
        "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
        "    \n",
        "    cluster1 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
        "    cluster2 = np.repeat(np.expand_dims(logs[compadre].values,1), 100, 1)\n",
        "    \n",
        "    f, ax = plt.subplots(nrows=1, ncols=7, figsize=(9, 12))\n",
        "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
        "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
        "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
        "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
        "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
        "    im1 = ax[5].imshow(cluster1, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im2 = ax[6].imshow(cluster2, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    \n",
        "    divider = make_axes_locatable(ax[6])\n",
        "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
        "    cbar=plt.colorbar(im2, cax=cax)\n",
        "    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
        "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
        "                                ' PS ', ' BS ']))\n",
        "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
        "    \n",
        "    for i in range(len(ax)-2):\n",
        "        ax[i].set_ylim(ztop,zbot)\n",
        "        ax[i].invert_yaxis()\n",
        "        ax[i].grid()\n",
        "        ax[i].locator_params(axis='x', nbins=3)\n",
        "    \n",
        "    ax[0].set_xlabel(\"GR\")\n",
        "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
        "    ax[1].set_xlabel(\"ILD_log10\")\n",
        "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
        "    ax[2].set_xlabel(\"DeltaPHI\")\n",
        "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
        "    ax[3].set_xlabel(\"PHIND\")\n",
        "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
        "    ax[4].set_xlabel(\"PE\")\n",
        "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
        "    ax[5].set_xlabel('Facies')\n",
        "    ax[6].set_xlabel(compadre)\n",
        "    \n",
        "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
        "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
        "    ax[5].set_xticklabels([])\n",
        "    ax[6].set_xticklabels([])\n",
        "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeyuM9RRoxxF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compare_facies_plot(blind, 'Prediction', facies_colors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnWfkNzXoxxI",
        "colab_type": "text"
      },
      "source": [
        "## Applying the classification model to new data\n",
        "\n",
        "Now that we have a trained facies classification model we can use it to identify facies in wells that do not have core data.  In this case, we will apply the classifier to two wells, but we could use it on any number of wells for which we have the same set of well logs for input.\n",
        "\n",
        "This dataset is similar to the training data except it does not have facies labels.  It is loaded into a dataframe called `test_data`.\n",
        "\n",
        "采用测试集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVkTFpzioxxI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "well_data = pd.read_csv('/content/tutorials-2016/1610_Facies_classification/validation_data_nofacies.csv')\n",
        "well_data['Well Name'] = well_data['Well Name'].astype('category')\n",
        "well_features = well_data.drop(['Formation', 'Well Name', 'Depth'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbG5ihyHoxxL",
        "colab_type": "text"
      },
      "source": [
        "The data needs to be scaled using the same constants we used for the training data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLuZKKPNoxxL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_unknown = scaler.transform(well_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emrz6E8ToxxR",
        "colab_type": "text"
      },
      "source": [
        "Finally we predict facies labels for the unknown data, and store the results in a `Facies` column of the `test_data` dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Uazem6ZoxxS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#predict facies of unclassified data\n",
        "y_unknown = clf.predict(X_unknown)\n",
        "well_data['Facies'] = y_unknown\n",
        "well_data\n",
        "#把预测的结果放到最后"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXPETq5ooxxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "well_data['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oK2NRDBvoxxX",
        "colab_type": "text"
      },
      "source": [
        "We can use the well log plot to view the classification results along with the well logs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0fGMeaEoxxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "make_facies_log_plot(\n",
        "    well_data[well_data['Well Name'] == 'STUART'],\n",
        "    facies_colors=facies_colors)\n",
        "\n",
        "make_facies_log_plot(\n",
        "    well_data[well_data['Well Name'] == 'CRAWFORD'],\n",
        "    facies_colors=facies_colors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8MzmDy4oxxZ",
        "colab_type": "text"
      },
      "source": [
        "Finally we can write out a csv file with the well data along with the facies classification results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEf1K2mxoxxa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "well_data.to_csv('/content/drive/My Drive/Android/结果.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsE3xm5loxxc",
        "colab_type": "text"
      },
      "source": [
        "## References\n",
        "\n",
        "Amato del Monte, A., 2015. Seismic Petrophysics: Part 1, *The Leading Edge*, 34 (4). [doi:10.1190/tle34040440.1](http://dx.doi.org/10.1190/tle34040440.1)\n",
        "\n",
        "Bohling, G. C., and M. K. Dubois, 2003. An Integrated Application of Neural Network and Markov Chain Techniques to Prediction of Lithofacies from Well Logs, *KGS Open-File Report* 2003-50, 6 pp. [pdf](http://www.kgs.ku.edu/PRS/publication/2003/ofr2003-50.pdf)\n",
        "\n",
        "Dubois, M. K., G. C. Bohling, and S. Chakrabarti, 2007, Comparison of four approaches to a rock facies classification problem, *Computers & Geosciences*, 33 (5), 599-617 pp. [doi:10.1016/j.cageo.2006.08.011](http://dx.doi.org/10.1016/j.cageo.2006.08.011)"
      ]
    }
  ]
}